Datasets:

adsabs

/

FOCAL

like 1

2015MNRAS.446..330W__Milgrom_2009c_Instance_1

Consider a space–time scale invariance of the equations of motion under the consideration of the transformation in Minkowsky space (Milgrom 2009a; see also Milgrom 2014a,b): (3) \begin{equation} (t,{\boldsymbol r}) \rightarrow (\lambda t,\lambda {\boldsymbol r}), \end{equation} where t and ${\boldsymbol r}=(x,y,z)$ are time and Cartesian coordinates, respectively, and λ is a positive number. The Newtonian gravitational acceleration for a spherically symmetric system, (4) \begin{equation} g_{\rm N} = \frac{GM_{\rm b}}{r^2}, \end{equation} then transforms as gN → λ−2gN, whereas the kinematical acceleration, $g\equiv {\rm d} \dot{x} / {\rm d} t$, scales as g → λ−1g. Here Mb( r) is the enclosed baryonic mass within r. As a result, the Newtonian gravitational acceleration and the kinematical acceleration scale differently under equation (3). Linking purely gravitational interactions to symmetries such as defined in equation (3) suggests deeper physics and constitutes a motivation for viewing MOND as much more than a mere phenomenological description of galactic dynamics (Milgrom 2009c). In order to assure that both the gravitational and the kinematical accelerations scale symmetrically under equation (3), i.e. in order to maintain the invariant symmetry, the gravitational acceleration, g, has to scale proportionally to $g_{\rm N}^{1/2}$. In order to obtain the correct dimension, a constant with the unit of acceleration, needs to be introduced. This constant is referred to as a0, such that (5) \begin{equation} g=(a_0g_{\rm N})^{1/2}, \end{equation} i.e. g2 = a0gN. Thus g = (GMba0)1/2/r, and the circular velocity, which follows from the centrifugal acceleration g = v2/r, is (6) \begin{equation} v = (GM_{\rm b}a_0)^{1/4}={\rm constant}, \end{equation} which is exactly the BTFR (Milgrom 1983a, 2009a, 2014b; McGaugh et al. 2000; Famaey & McGaugh 2012). We refer to gravitational dynamics which thus conforms to low-acceleration scale invariance (equation 3) as low-acceleration SID. SID beautifully reproduces the deep MOND equations of motion. It is rather remarkable that such a simple principle as SID and discovered by Milgrom leads to one of the most important scaling relations which real galaxies are observed to obey. Note that equation (6) implies that each baryonic galaxy is surrounded by a logarithmic non-particle (and thus phantom) DM halo potential, which is however not a real halo as it is only evident if Newtonian dynamics is applied to the galaxy. If SID is true, then a Newtonian observer would thus deduce that each baryonic galaxy is surrounded by a PDMH the mass of which is proportional to radial distance (equation 27 below).

2021MNRAS.505..515Z__Naul_et_al._2018_Instance_2

For variable star classification, both convolutional neural networks (CNNs; LeCun, Bengio & Hinton 2015) and recurrent neural networks (RNNs; Hochreiter & Schmidhuber 1997; Cho et al. 2014) have been shown to be competitive to the traditional RF-based methods. Naul et al. (2018) used an RNN autoencoder network to learn low-dimensional representations of period-folded light curves in an unsupervised fashion. This representation was then, in a supervised context, used as feature inputs to an RF classifier. They showed that the learned features are at least as good as, and often better than, two sets of state-of-the-art hand-crafted features (Richards et al. 2011; Kim & Bailer-Jones 2016), in terms of downstream classification accuracy. Becker et al. (2020) used an RNN for which instead of period-folding, each input light curve is grouped with a moving time-sample window of size 50 and stride 25. Although period-folding improves performance (Naul et al. 2018), Becker et al. (2020)’s time-space RNN does not require the period to be calculated, and is thus less computationally expensive in terms of preprocessing. Again, they found similar performance to an RF classifier with the Nun et al. (2015) features over three data sets, although lower accuracy was seen for many sub-classes with the OGLE data set (Table 2; see Section 3.3 for data description). More recently, Jamal & Bloom (2020) systematically benchmarked the performance of different configurations of RNN and CNN network architectures on variable star classification. Aside from other work (e.g. Aguirre, Pichara & Becker 2018; Tsang & Schultz 2019) evaluating neural network (NN) performance retrospectively on previously labelled data sets, Dékány & Grebel (2020) used an RNN classifier to identify a new sample of fundamental-mode RR Lyrae (RRab) stars. Similarly, Dékány et al. (2019) found Classical and Type II Cepheids with a CNN classifier, also using the VISTA Variables in the Via Lactea (VVV) survey (Minniti et al. 2010) and using period-folded light curves.

2021AandA...646A..96C__Brusa_et_al._2018_Instance_2

AGN-driven outflows. Another possible effect of the AGN activity on the molecular gas is through outflows. This possibility is supported by observations of individual objects: For example, Carniani et al. (2017), Brusa et al. (2018) and Loiacono et al. (2019) find low gas fractions in powerful AGN at cosmic noon hosting high-velocity molecular and ionized outflows (but see also Herrera-Camus et al. 2019). AGN feedback in action in these targets could be depleting the molecular gas reservoir (Brusa et al. 2015). Förster Schreiber et al. (2019), studying outflows in a large sample of 0.6  z  2.7 galaxies through integral field spectroscopy of the Hα emission line, find that incidence, strength, and velocity of AGN-driven winds are strongly correlated with the stellar mass. In particular, they find that high-velocity (∼1000–2000 km s−1) AGN-driven outflows are commonly detected at masses above log(M*/M⊙) = 10.7, and present in up to 75% of the population for log(M*/M⊙) > 11.2. Interestingly, above this stellar-mass threshold we find a significant CO luminosity deficit in our AGN sample with respect to inactive galaxies (Fig. 3, bottom). Moreover, our AGN show on average gas fractions 0.57 dex (by using uniform assumptions, Sect. 4) lower than inactive galaxies at the 2.2σ level. Quantitatively, this translates into Mgas, mol/M* ≈ 0.3 for AGN (0.16 if we use r31 = 0.92; Kirkpatrick et al. 2019) and ≈1 in inactive galaxies. This representative value for our AGN is in line but not as low as previous work targeting extremely powerful sources (e.g., Mgas, mol/M*  0.05 in Brusa et al. 2018). Our team is performing a systematic investigation of ionized gas outflows with SINFONI as part of the SUPER survey, and 11 targets of our ALMA sample have complementary good quality SINFONI data (Kakkad et al. 2020; Perna et al., in prep.). For some of them we measured [O III] line widths larger than 600 km s−1, interpreted as a clear signature of the presence of an AGN-driven outflow in these objects (Kakkad et al. 2020). A detailed comparison between outflow and CO properties for these targets will be presented in a future work. Distinguishing among the scenarios described above is challenging with the current dataset. AGN feedback could proceed in different ways and different mechanisms likely overlap in shaping the properties of the molecular gas reservoir. For example, AGN radiation could both heat and/or dissociate CO molecules. In this case, AGN would produce a feedback mechanism that does not require outflows but would potentially work toward inhibiting further star formation. As for AGN-driven outflows, they could impact the gas content by ejecting material out of the galaxy (e.g., Travascio et al. 2020), or they could produce CO heating or dissociation due to shocks. Additionally, numerical simulations predict that AGN-driven outflows may heat via shocks a significant quantity of the gas in the ISM, reaching the high temperatures required for the excitation of high-J CO transitions (Costa et al. 2018). To reach a deeper understanding of the impact of AGN on the molecular gas reservoir, also on longer timescales, predictions from simulations providing the spatial scales and effects of AGN activity on CO properties as a function of cosmic time are needed.

2022MNRAS.512..186K__Datta_et_al._2010_Instance_1

This work is the second in a series of works aimed at understanding the effect of residual gain errors in different power spectrum estimations in the presence of strong foreground and exploring potential mitigation techniques. In this work, we have not investigated the different possibilities for the presence of residual gain errors and chosen the values of σR and σI same as σg for most of the discussions. In general, the standard deviation of the real and imaginary parts can be different. We observe here that it is essential to assess the time dependence of the gain accurately, as its inaccurate estimation leads to the time-correlated residual gain errors. In this work, we do not consider the effect of frequency correlation in gain error, and all our estimations are done for correlating visibilities in the same frequency channel. Furthermore, this work also uses the foreground subtraction technique, where we expect to have accurate knowledge of the foreground emissions (Jelić et al. 2008; Ghosh et al. 2012). An alternative method, more regularly exercised in literature, is foreground avoidance. It has been established that the foregrounds to the redshifted 21-cm emissions remain correlated across relatively larger bandwidth (Platania et al. 1998; Santos et al. 2005; Ali et al. 2008; Jelić et al. 2008; Chakraborty et al. 2019b), whereas the H i signal decorrelates faster (Bharadwaj & Pandey 2003; Bharadwaj & Ali 2005). As a result, when the power spectrum is observed as a function of (k∥, k⊥), the foreground emission remains concentrated near the low k∥, inside the ‘wedge’ (Datta et al. 2010; Morales et al. 2012; Vedantham et al. 2012). Note that the smaller frequency separation in multifrequency angular power spectrum contributes to larger k∥ modes of the power spectrum. Hence, the effect of frequency-independent residual gain we see here at zero frequency separation may contribute to bias in the power spectrum beyond the wedge. Moreover, the antenna-based gains are functions of both time and frequency; the residual gain is expected to have correlated frequency dependence. Such frequency-correlated calibration errors couple the foreground power beyond the foreground wedge into the EoR window region of the 2D power spectrum space (Barry et al. 2016; Ewall-Wice et al. 2017; Byrne et al. 2019; Pal et al. 2021). At present, we are working towards expanding the formalism presented in this paper to estimate bias and variance in power spectrum estimate when visibility correlation in different frequencies is considered. Here, we also consider that the gain errors arising from the different antennae are uncorrelated. Though this is a fairly good assumption for the gain arising from electronics in the antenna system itself, the ionospheric effects may introduce correlated gains across the antenna. Furthermore, as the calibration procedure uses baseline-dependent gains to solve for the antenna dependent gains, calibration errors can lead to correlated residual gain errors across the antenna. Moreover, asymmetry in the telescope aperture, mechanical fatigue of telescope structure, etc., can lead to parts of the gain errors correlated across different antennae and even across different days of observations. We are investigating these effects, and the result will be presented in future work. Though the demonstrations here are done with the uGMRT as a model for the interferometer, a similar analysis can be carried out for any telescope of concern, and a prior assessment of the effect of the gain errors can be made using the analytical expression presented here with minimum computation cost. Furthermore, this work emphasizes the importance of estimating and establishing the gain statistics for a given interferometer. Though we use a simple model for the residual gain error here, the calculations that lead to the analytical expression can be readily expanded for a more complicated gain error model. We believe that this work provides significant direction in understanding and planning observations to detect redshifted 21-cm power spectrum.

2021AandA...656A..95P__Houdek_et_al._2017_Instance_1

Many efforts have thus been devoted to the correction of surface effects, either from theoretical modelling (e.g., Gabriel et al. 1975; Balmforth 1992b; Houdek 1996; Rosenthal et al. 1999; Grigahcène et al. 2005) or through empirical formulae (e.g., Kjeldsen et al. 2008; Christensen-Dalsgaard 2012; Ball & Gizon 2014; Sonoi et al. 2015). Some aspects, however, are very complicated to model, and existing models make use of assumptions that can barely be justified, if at all. For instance, turbulent pressure modulations are usually described in the Gas-Γ1 (GGM) or reduced-Γ1 (RGM) approximations (Rosenthal et al. 1999), which amounts to neglecting the effects of turbulent dissipation and buoyancy on the mode (Belkacem et al. 2021). Another problem is the use of time-dependent mixing-length formalisms (Unno 1967; Gough 1977) to account for modal surface effects (e.g., Gabriel et al. 1975; Houdek 1996; Grigahcène et al. 2005; Sonoi et al. 2017; Houdek et al. 2017, 2019). While useful for the bulk of the convective region, the mixing-length hypothesis is no longer valid in the superadiabatic region just beneath the surface of the star, as shown by 3D hydrodynamic simulations of stellar atmospheres (see Nordlund et al. 2009, for a review). Finally, such formalisms require that the oscillations be separated from the convective motions, thus yielding separate equations. This is done either by assuming a cut-off in wavelength space, with oscillations having much shorter wavelengths than turbulent convection (Grigahcène et al. 2005), or by using 3D hydrodynamic simulations and separating the oscillations from convection though horizontal averaging (Nordlund & Stein 2001). The necessity to separate the equations for oscillations and convection is fundamentally problematic as there is no rigorous way to disentangle the two components, mainly because, in solar-type stars, they have the same characteristic lengths and timescales (Samadi et al. 2015). This is even truer if one wishes to model their mutual coupling.

2022ApJ...935...49G__Hezaveh_et_al._2016_Instance_1

Strong gravitational lensing systems are a powerful tool for cosmology. They have been used to study how dark matter is distributed in galaxies and clusters (e.g., Kochanek 1991; Hogg & Blandford 1994; Broadhurst et al. 2000; Koopmans & Treu 2002; Bolton et al. 2006; Koopmans et al. 2006; Bradač et al. 2008; Huang et al. 2009; Vegetti & Koopmans 2009; Jullo et al. 2010; Grillo et al. 2015; Shu et al. 2015, 2016, 2017; Meneghetti et al. 2020) and are uniquely suited to probe the low end of the dark matter mass function and test the prediction of the cold dark matter (CDM) model beyond the local universe (e.g., Vegetti et al. 2010, 2012; Hezaveh et al. 2016; Ritondale et al. 2019; Diaz Rivero & Dvorkin 2020; Caǧan Sengül et al. 2020, 2021; Gilman et al. 2021). Multiply lensed supernovae (SNe) are ideal for measuring time delays and H 0 because of their well-characterized light curves, and in the case of Type Ia, with the added benefit of standardizable luminosity (Refsdal 1964; Treu 2010; Oguri & Marshall 2010), provided microlensing can be accurately characterized (Yahalomi et al. 2017; Foxley-Marrable et al. 2018). Furthermore, SNe have the benefit of fading, so for these systems lens models can be validated using images that are uncontaminated by bright point sources (Ding et al. 2021). In recent years, strongly lensed SNe, both core-collapse (Kelly et al. 2015; Rodney et al. 2016) and Type Ia (Quimby et al. 2014; Goobar et al. 2017; Rodney et al. 2021), have been discovered. Time-delay H 0 measurements from multiply imaged SNe (e.g., Goldstein & Nugent 2017; Shu et al. 2018; Goldstein et al. 2018, 2019; Pierel & Rodney 2019; Suyu et al. 2020; Huber et al. 2022), combined with measurements from distance ladders (e.g., Freedman et al. 2019, 2020; Riess et al. 2019, 2022) and lensed quasars (e.g., Suyu et al. 2010, 2013; Treu & Marshall 2016; Bonvin et al. 2017; Birrer et al. 2020; Millon et al. 2020; Wong et al. 2020), can be an important test of the tension between H 0 measured locally and the value inferred from the cosmic microwave background (CMB; Planck Collaboration et al. 2020).

2020ApJ...901L..10L__White_et_al._1992_Instance_1

The 17 GHz polarization reversal during this CRF can be a yet unknown feature inherent to the fan–spine structure, where magnetic polarity around null point (NP) varies so rapidly as to affect the propagation of microwave polarization. A way to possibly explain this polarization change is to view it as a mode-coupling phenomenon, the process by which the rays reverse their original sense of polarization while passing through a quasi-transverse field region along the line of sight from the radiation source to the observer, depending on the degree of mode coupling there (Cohen 1960; Zheleznyakov 1970; Melrose 1975; White et al. 1992). This is an attractive scenario for a fan–spine structure, because the fan surface may well act as a quasi-transverse layer for the rays emitted underneath. To think about an ideal fan–spine structure with a flux rope inside, in this configuration, the magnetic fields above the fan surface are all in the negative magnetic polarity, and the rays emitted from either magnetic polarity underneath will be observed as LHCP everywhere. Therefore, the LHCP observed everywhere before the flare can simply be due to the fan–spine structure, without any strong mode-coupling phenomenon. On the other hand, if a magnetic flux rope rises to reconnect with the overlying fan field, the fan surface may partially open up to let the flux rope erupt out. Such a change of magnetic field structure can explain the instant reversal of the 17 GHz polarization at t3 more naturally. The reconnection between the magnetic fields inside and outside of the fan will occur across a current sheet, the so-called breakout current sheet (BCS), and the newly open field lines amount to the lower part of the rising and expanding BCS (see, e.g., Lynch et al. 2016; Karpen et al. 2017). A sustained BCS over the active region might affect the microwave polarization, as mode coupling across a current sheet is still a debatable issue (Zheleznyakov et al. 1996; Lee et al. 1998; Lee 2007). We here offer only the simplest interpretation, according to which the change from LHCP to RHCP of the 17 GHz emission over the inner ribbon is not just a signature for any magnetic field perturbation, but may indicate a specific form of a breakout eruption out of the closed fan structure. The implied magnetic field reconfiguration is in line with the recently reported decay of the coronal magnetic field at the flare site by Fleishman et al. (2020).

2017MNRAS.464.4495G__Levine_et_al._2006_Instance_1

To emphasize it again, one may suggest accordingly that several low-m modes of moderately growing collective oscillation with different amplitudes, number of spirals and pitch angles (radial wavelengths), including the most interesting m = 1 mode, may co-exist in the solar neighbourhood. The so-called lopsidedness, or the m = 1 azimuthal asymmetry, is often seen in the distribution of stars and gas in the outer discs of many disc galaxies (Zaritsky & Rix 1997; van Eymeren et al. 2011). Generally, there is no azimuthal symmetry, at least far from their centres, in multi-arm galaxies (Efremov 2011). As for us the spiral pattern of the Galaxy is not a single m model but a superposition of modes as suggested theoretically in the late 1970s (Bertin et al. 1977; Lin & Lau 1979; Bertin 1980; see also Griv & Wang 2014). The overall pattern is thus basically multiple armed. In addition, we argue that the Galaxy is the azimuthally asymmetric spiral system (cf. Levine et al. 2006). The system therefore may exhibit a complicated asymmetric, multi-arm, not well-defined spiral structure as seen, for instance, in the face-on spiral galaxy M101 (Fig. 13). Interestingly, many spiral structures in galaxies do not appear to be well-organized grand designs. Galaxies dominated by a single and symmetric pattern are exceedingly rare. The pitch angle of the spiral pattern seems to be relatively small for all models considered, |tan p| ≪ 1. Thus, the original Lin–Shu approximation of tightly wound gravity perturbations used throughout the theory does not fail. The relative amplitude of the surface density $\widetilde{\Sigma }_0/\Sigma _{\mathrm{basic}} \gtrsim 1$ for all models considered. The latter means that the non-axisymmetric variation found in the relative amplitude of the surface density does not represent a small perturbation in the basic equilibrium state of the Galaxy that is axisymmetric in the mean. The above mentioned results are in agreement with our previous determinations (Griv et al. 2013, 2014, 2015a,b,c).

2017MNRAS.469.3252P__Anderson_&_Bedin_2010_Instance_1

Tol1247 was imaged with the HST (see Fig. 3) in the optical using the Wide Field Ultraviolet-Visible Channel (UVIS) of its Wide Field Camera 3 (WFC3). For UV imaging, the Advanced Camera for Surveys' Solar Blind Channel (ACS/SBC) was used. Seven filters were utilized in total, allowing to apply laxs – the Lyman alpha eXtraction software (Hayes et al. 2009) – to produce continuum-subtracted Lyα, Hα and Hβ images, corrected for underlying stellar absorption and contamination from [N ii] λ6548, 6584. The latter one is based on the spectroscopic line ratio ${[\mathrm{N}\,\small {II}]}/{\mathrm{H}\alpha }=0.0605$ published by Terlevich et al. (1993). The imaging strategy and data reduction methodology for Tol1247 is very similar to that of the Lyman Alpha Reference Sample (LARS; Hayes et al. 2014; Östlin et al. 2014; Duval et al. 2015) and the basic data reduction for this data set is done in the same way as for LARS. Flat-field-corrected frames were obtained from the Mikulski Archive for Space Telescope. The charge transfer inefficiency (CTE) correction for the ACS data was performed by the pipeline, whereas CTE losses in WFC3/UVIS (Anderson & Bedin 2010) were treated manually using the tools supplied by STScI.3 We then stacked the individual data frames and drizzled them to a pixel scale of 0.04 arcsec pixel−1 using DrizzlePac version 1.1.16 (Gonzaga et al. 2012). Further pre-processing of the data includes additional masking of cosmic rays in the drizzled frames and matching the point spread functions (PSFs) for the different filters. In order to match the PSF, we first construct PSF models for all of the filters used in the study. For the optical filters, we use TinyTim models (Krist, Hook & Stoehr 2011), re-sampled to a pixel scale of 0.04 arcsec pixel−1. However, for the FUV filters, TinyTim is not accurate enough, particularly in the wings. Therefore, the PSF models for the FUV filters are instead built from stacks of stars obtained in calibration observations; see Hayes et al. (2016) for details. All of the PSF models are then normalized by peak flux and stacked by maximum pixel value. We then proceed to calculate convolution kernels that match the PSFs for all of the filters to the maximum width model. Each kernel is built up from a set of delta functions and we find the optimum matching kernel by least-squares optimization; see also Becker et al. (2012) and Melinder et al. (in preparation). The drizzled and registered images are convolved with the kernel found for each filter, which result in a set of images matched to a common PSF.

2020ApJ...902...77O__Wuyts_et_al._2012_Instance_1

Guo et al. (2015) used an automated “blob finder” to identify star-forming regions in the HST/ACS images for 3,239 log M*/M⊙ 10.6 galaxies in CANDELS (GOODS-S and UDS fields) at 0.5 z 3. They defined clumps as blobs which contribute more than 8% of the total UV light of their host galaxies. In contrast to our results, they find that a much higher fraction of SFGs are clumpy (as much as ∼60% at z ∼ 2), and also that higher mass bins have lower clumpy fractions. It is worth pointing out that clumpy galaxy fractions are highly sensitive to methodology and clump definition and vary widely in the literature (e.g., Ravindranath et al. 2006; Elmegreen et al. 2007; Wuyts et al. 2012; Guo et al. 2015), so we should not expect complete agreement. Comparing their observed clumpy galaxy fractions (as a function of redshift) to fractions derived from the K15 classification scheme, Guo et al. (2015) find that their results agree best with clumpy fractions derived using both the clumpiness and patchiness flags from the K15 data release (see their Appendix A) rather than either the clumpy or patchy flags alone. This is because the blob finder does not account for the light concentration of the blobs. The inclusion of patches may help explain why their clumpy fractions are generally higher than ours. Guo et al. (2015) also exclude very small (01) and elongated (axis ratio 0.5) galaxies from their sample. Our inclusion of such galaxies could easily lead to lower clumpy fractions given that many galaxies with half-light radius 01 are Spheroids (see Figure 13), which rarely possess clumps. We also include edge-on disks whose clumps may be obscured by dust. The inclusion of galaxies with unresolved or obscured clumps may imply that we are underestimating the clumpy fractions. However, we do include the contribution from non-disky compact or irregular SFGs which would be excluded by the Guo et al. (2015) cuts. The contribution from such galaxies is not insignificant, especially at z ∼ 2, so our looser selection is not without merit. Even if we do underestimate our clumpy fractions, our consistent sample selection and methodology ensures that they should be similarly underestimated at all redshifts, preserving the general evolutionary trends.

2016AandA...588A..48C__Huré_(2013)_Instance_1

The major difference between the axisymmetric case and the asymmetric approach is that we need to derive the 3D, asymmetric gravitational potential of luminous baryons beforehand. We thus computed the potential in Cartesian coordinates (x,y,z), which enables us to derive both radial and azimuthal forces at any desired z. This can be derived independently for each stellar or gaseous contribution. The gravitationtal potential Φ of the mass distribution is in principle deduced from the convolution of the volume mass density by the Green function, namely (11)\begin{equation} \Phi = -G \iiint\frac{{\rm d}\rho'}{|\vec{r}-\vec{r}'|}, \label{eq:pot} \end{equation}Φ=−G$dρ′|r−r′|,where G is the gravitational constant. However, the Green function written by 1/ | r−r′ | = [(x−x′)2 + (y−y′)2 + (z−z′)2] − 1/2, is well known to diverge at each point where x = x′, y = y′ and z = z′. This function renders any direct estimate of Φ inaccurate and generally encourages modelers to incorporate a softening length to bypass the divergence. Here, we use the new formalism presented in Huré (2013) who showed that the Newtonian potential is exactly reproduced by using an intermediate scalar function ℋ, namely \hbox{$\Phi = \partial_{xy}^2 {\cal H}$}Φ=∂xy2ℋ. In 3D, this hyperpotential is written as (12)\begin{equation} {\cal H}(x,y,z) = -G \iiint_{\Omega'}{\rho(x',y',z')} \kappa^{xy}(X,Y,Z){\rm d}x'{\rm d}y'{\rm d}z' , \end{equation}ℋ(x,y,z)=−G$Ω′ρ(x′,y′,z′)κxy(X,Y,Z)dx′dy′dz′,with X = x−x′, Y = y−y′ and Z = z−z′. The κ function is a hyperkernel defined by (13)\begin{equation} \kappa^{xy}(X,Y,Z) = -Z \arctan \frac{XY}{Z |\vec{r}-\vec{r}'|}+Y \ln \frac{X+|\vec{r}-\vec{r}'|}{\sqrt{Y^2+Z^2}}\cdot \label{eq:k} \end{equation}κxy(X,Y,Z)=−ZarctanXYZ|r−r′|+YlnX+|r−r′|Y2+Z2·This approach is particularly simple and efficient for 2D or 3D distributions since ℋ is, in contrast to Φ, the convolution of the surface or volume density with a regular, finite amplitude kernel. The methodology thus does not make use of a softening length in the derivation of the potential. In practice, this convolution is performed using the second-order trapezoidal rule and the mixed derivatives are estimated at the same order from centered finite differences. Furthermore, the volume density of the tracer are deduced from a surface density map, considering that the vertical density follows a sech-squared or exponential law of constant scaleheight with radius. The precision of these schemes is sufficient for the present purpose.

2015ApJ...804..130C___2013_Instance_2

We have developed the simplest spherical void lens model based on the recently developed embedded lens theory. We have assumed a uniform mass profile for the void, compensated by a thin bounding shell. The infinitesimally thin bounding shell was chosen for convenience (Maeda & Sato 1983a, 1983b). To investigate other void profiles such as a non-uniform void interior or a finite-thin bounding ridge (Colberg et al. 2005; Lavaux & Wandelt 2012; Pan et al. 2012; Sutter et al. 2012; Hamaus et al. 2014; Kantowski et al. 2015) is straightforward; one has only to evaluate the Fermat potential of Equation (1) or equivalently the potential part of the time delay of Equation (4). It is also possible to build embedded void lens models with non-spherically symmetric density profiles given that the lowest-order embedded lens theory is applicable to any distributed lens (Kantowski et al. 2013). It is well accepted by the lensing community that small overdensities attract light, whereas small underdensities repel light. This fact can be rigorously proved using general relativistic perturbation theory (Sachs & Wolfe 1967) assuming . However, the repulsive nature of lensing by a large and deep underdense region (i.e., cosmic voids) as described by the rigorously derived but simply implemented embedded lens formalism did not appear until Kantowski et al. (2013). In the case of large density contrasts, i.e., approaching its lower bound −1 for cosmic voids, the repulsive lens equation follows naturally from the embedded lensing theory. This theory is based on Swiss cheese models (Einstein & Straus 1945), which are exact solutions of Einstein’s field equations containing inhomogeneities with large density contrasts (Chen et al. 2010, 2011, 2015; Kantowski et al. 2010, 2012, 2013). The void-lensing community takes void repulsive lensing as granted (e.g., Amendola et al. 1999; Das & Spergel 2009), whereas the galaxy/cluster strong-lensing community has ignored embedding effects, i.e., the repulsive lensing caused by the large underdense regions surrounding the central overdense lens. Besides correctly predicting repulsive lensing by cosmic voids, our Fermat potential formulation can be used to compute the void-lensing time delay effects, including the ISW effect caused by voids; see Equation (5).

2016AandA...587A.159G__Segura_et_al._2007_Instance_1

One has to be sure to rule out cases where inorganic chemistry can mimic the presence of life (“false positives”). Potential abiotic ozone production on Venus- and Mars-like planets has been discussed by Schindler & Kasting (2000, and references therein). While this is based on photolysis of e.g., CO2 and H2O and is thus limited in extent, a sustainable production of abiotic O3 which could build up to a detectable level has been suggested by Domagal-Goldman & Meadows (2010) for a planet within the habitable zone of AD Leonis with a specific atmospheric composition. Indeed, other studies confirm that abiotic buildup of ozone is possible (e.g., Hu et al. 2012; Tian et al. 2014); however, detectable levels are unlikely if liquid water is abundant, as e.g. rainout of oxidized species would keep atmospheric O2 and O3 low (Segura et al. 2007), unless the CO2 concentration is high and both H2 and CH4 emissions are low (Hu et al. 2012). False-positive detection of molecules such as CH4 and O3 is discussed by von Paris et al. (2011). Seager et al. (2013) present a biosignature gas classification. Since abiotic processes cannot be ruled out for individual molecules (e.g. for O3), searches for biosignature molecules should search for multiple biosignature species simultaneously. It has been suggested that the simultaneous presence of O2 and CH4 can be used as an indication for life (Sagan et al. 1993, and references therein). Similarly, Selsis et al. (2002) suggest a so-called “triple signature”, where the combined detection of O3, CO2 and H2O would indicate biological activity. Domagal-Goldman & Meadows (2010) suggest to simultaneously search for the signature of O2, CH4, and C2H6. Of course, care has to be taken to avoid combinations of biosignature molecules which can be generated abiotically together (see e.g. Tian et al. 2014). The detectability of biosignature molecules is discussed, e.g. by von Paris et al. (2011) and Hedelt et al. (2013). In particular, the simulation of the instrumental response to simulated spectra for currently planned or proposed exoplanet characterization missions has shown that the amount of information the retrieval process can provide on the atmospheric composition may not be sufficient (von Paris et al. 2013).

2022ApJ...931...70B___2022a_Instance_1

RFs can propagate from the magnetotail to Earth over a long distance more than 10 R E together with BBFs behind them (Runov et al. 2009; Cao et al. 2010). Studies have suggested that RFs are crucial regions for particle acceleration, pitch-angle evolution, wave–particle interactions, and electromagnetic energy conversion during their Earthward propagation. For instance, rapid increases in energy fluxes of electrons and ions from tens to hundreds of keV are a typical feature of RF events (Khotyaintsev et al. 2011; Liu et al. 2013, 2018c, 2021a, 2022b; Zhou et al. 2018; Liu & Fu 2019; Gabrielse et al. 2021), pitch-angle distribution of suprathermal electrons can evolve dramatically around RFs (Runov et al. 2013; Liu et al. 2020), strong particle and wave activity can occur in the vicinity of RFs (Ono et al. 2009; Zhou et al. 2009, 2014; Fu et al. 2014; Breuillard et al. 2016; Greco et al. 2017; Yang et al. 2017), and RFs are associated with energy conversion from electromagnetic fields to particles (Sitnov et al. 2009; Huang et al. 2015; Khotyaintsev et al. 2017; Liu et al. 2018a, 2022a). The energetic plasma in the vicinity of RFs plays a key role in connecting the magnetotail with the inner magnetosphere because they carry a large amount of energy and can be injected into the inner magnetosphere to affect the ring current and radiation belt (Gabrielse et al. 2012; Duan et al. 2014; Turner et al. 2014). Possible mechanisms responsible for the energization of particles around RFs have been widely investigated based on both spacecraft observations and numerical simulations during the past decade. The strong convection electric field induced by the strong magnetic field gradient of RFs provides significant adiabatic acceleration of the ambient particles (Birn et al. 2004, 2013, 2015; Gabrielse et al. 2012, 2014, 2016; Ganushkina et al. 2013; Liu et al. 2016; Turner et al. 2016). Nonadiabatic effects, caused by particle reflection ahead of the RFs (Zhou et al. 2018), resonance with RFs (Ukhorskiy et al. 2013, 2017), and scattering by wave emissions (Zhou et al. 2009; Greco et al. 2017), are also significant for particle energization. These above studies usually assumed that the RF surface has a planar boundary at a typical thickness comparable to the ion gyroradius and below (Nakamura et al. 2002; Sergeev et al. 2009; Zhou et al. 2009; Schmid et al. 2011; Liu et al. 2013; Vapirev et al. 2013). Divin et al. (2015b) revealed that the RF surface is unstable to instabilities ranging from electron scales to ion scales. Simulation studies found that RFs can be unstable to interchange instability and that finger-like structures on ion–electron hybrid scales can develop at the RF (Vapirev et al. 2013). Such finger-like structures are found to play a role in modulating the electron acceleration process (Wu et al. 2018). Bai et al. (2022) also reported significant ion trapping acceleration at the RF with ion-scale ripples. Unlike these surface structures with ion or ion–electron hybrid scales, Liu et al. (2018b) recently reported that the RF layer has electron-scale density gradients, currents, and electric fields, based on the MMS mission, which consists of four spacecraft separated by 30 km. Such electron-scale ripple structure can be generated by lower hybrid drift instability (Divin et al. 2015b; Pan et al. 2018). Liu et al. (2021c) presented a detailed investigation of energy flux densities at two RFs with/without the electron-scale surface ripples and indicated that surface ripples may play an important role in the particle dynamics. But how such electron-scale RF structure impacts the electron energization and transport still remains unknown. In this paper, with the aid of observation-based test-particle simulation, we aim to investigate in detail the effect of the front surface ripples on the local electron dynamics.

2016ApJ...817....9K__Chen_et_al._2006_Instance_2

To perform a statistical analysis of the average quiescent fraction of satellites around our sample of massive galaxies, we use a statistical background subtraction technique (e.g., Kauffmann et al. 2010; Tal et al. 2012; Wang & White 2012; Kawinwanichakij et al. 2014). We detect objects within fixed apertures centered on our central galaxies and satisfying Equation (2). These apertures include physically associated galaxies as well as chance alignments of foreground and background galaxies. We estimate and correct for the contamination due to chance alignments by placing random apertures across the field. We adapt this procedure by restricting the placement of the random apertures to regions near the centrals, as demonstrated by Chen et al. (2006). This accounts for the bias due to contaminating galaxies that are physically associated with the centrals but are not satellites (i.e., the 2-halo term of the correlation function; see Chen et al. 2006).15 15 The contaminating galaxies that are physically associated with the central galaxies in our sample are expected to have marginally different properties than truly random field galaxies due to the fact that they exist in biased regions of the universe. There may be an additional effect due to large-scale 2-halo conformity. If 2-halo conformity exists, our procedure effectively corrects for it. We therefore place the random apertures within annuli with inner and outer radii equal to 1 and 3 cMpc from each central galaxy for the UDS and UltraVISTA. Parenthetically, our tests showed that the restriction on the location of the background apertures has only a small effect on the conformity signal. Relative to apertures that are placed randomly through the field, this correction increases the quiescent fractions of background galaxies by 0.4%–10%. For the smaller ZFOURGE fields, placing the random apertures within annuli is too restrictive, and for this survey we randomly place the apertures across the fields. We do note that the ZFOURGE fields are small enough that even these randomly placed apertures trace the same large-scale environment as the centrals. Additionally, we find that when we restrict the background apertures to be cMpc from the centrals, it changes the measured quenching efficiencies (see Section 4 below) by 10%, and none of our conclusions would be changed.

2016ApJ...820...12P__Camenzind_&_Krockenberger_1992_Instance_1

Variability in observed emission can be considered a defining characteristic of active galactic nuclei (AGNs), and for the roughly 10% of AGNs that are radio-loud (e.g., Jiang et al. 2007) the majority of this variable emission is understood to arise from the relativistic flows of plasma along two oppositely directed jets (e.g., Urry & Padovani 1995). When viewed at small angles to the jet direction the Doppler boosting makes the emission from the approaching jet appear dramatically brighter and also shortens the observed timescales with respect to those in the emitted frame, thereby explaining many of the properties of blazars (e.g., Blandford & Rees 1978; Lister 2001; Gopal-Krishna et al. 2003). Multiband radio studies and very long baseline interferometry (VLBI), coupled with theoretical models, have provided extremely strong evidence for the presence of both moving and standing shocks in these jets (e.g., Marscher & Gear 1985; Hughes et al. 1991; Lister et al. 2001, 2009; Marscher et al. 2008, 2010), indicating significant changes in the fluid flow and/or the density of material ejected into the jets; it is now accepted that the largest flares arise from the production and relativistic propagation of new components seen as radio knots. Changes in the overall direction of the inner portions of the jet, or at least its brightest portions, have also been demonstrated via VLBI (e.g., Biretta et al. 1986; Ros et al. 2000; Piner et al. 2003; Caproni & Abraham 2004). Even modest changes in direction (e.g., Camenzind & Krockenberger 1992; Gopal-Krishna & Wiita 1992) have long been recognized as one way to produce significant changes in the flux and polarization (e.g., Gopal-Krishna & Wiita 1993; Piner et al. 2008). It is to be expected that turbulence is produced within these jets, at least in the vicinity of shocks, and thus some of the variations should arise from such smaller scale motions as has been suggested theoretically (Marscher & Travis 1991; Marscher 2014, Calafut & Wiita 2015) and strongly supported by observations of blazars (e.g., Marscher et al. 2008, 2010; Bhatta et al. 2013). In addition, there is the possibility that portions of the jet are moving much faster than other portions, and such misaligned mini-jets could also produce some extremely rapid fluctuations (Giannios et al. 2009; Biteau & Giebels 2012). Variability on a wide range of timescales can be produced within the accretion disks feeding the central black holes (e.g., Czerny 2006). This presumably dominates the variations from radio-quiet AGNs though not those of radio-loud ones because the special relativistic boosting of the jet emission is so important in the latter (Urry & Padovani 1995). Some of the variations in the jet emission might be traced to plasma fluctuations in the disk being advected into the base of the jets (e.g., Wiita 2006) but the exact origins of the initial fluctuations are not addressed in this work. Here we address the question of whether variations in the bulk flow of a propagating relativistic hydrodynamic (RHD) jet along with sub-grid mildly relativistic turbulence can produce light curves and power spectra resembling those of radio-loud AGNs.

2017MNRAS.465..492M__Taverna_et_al._2015_Instance_2

Given the quite strong surface magnetic field of the M7, thermal radiation is expected to be polarized, either if emission is from a bare surface or from an atmosphere (see Turolla et al. 2004; Potekhin 2014). The polarization properties are quite different in the two cases, although there are still uncertainties, especially at optical/ultraviolet (UV) wavelengths. One of the first predictions of quantum electrodynamics (QED), even before it was properly formulated, was vacuum birefringence, and, in particular, that a strong magnetic field affects the propagation of light through it (Heisenberg & Euler 1936; Weisskopf 1936). In thermally emitting INSs, radiation comes from a region comparable with the entire star surface, over which the magnetic field direction changes substantially. In the absence of QED vacuum polarization effects, this would produce a drastic depolarization of the radiation collected at infinity (Heyl, Shaviv & Lloyd 2003, see also Taverna et al. 2015; González Caniulef et al. 2016 and references therein). Vacuum birefringence dramatically increases the linear polarization of the observed radiation, from a level of a few  per cent up to even ∼100 per cent, depending on the viewing geometry and the surface emission mechanism (Heyl & Shaviv 2000, 2002; Heyl et al. 2003; Taverna et al. 2015; González Caniulef et al. 2016). Detecting polarization in the thermal emission from the surface of an INS will be therefore extremely valuable. First, and independently on the physical conditions of the emitting region, the detection of a large degree of linear polarization in the signal would constitute the observational evidence of QED vacuum polarization effects in the strong-field regime. Secondly, the polarization observables can be compared with emission models and help to uncover the physical conditions of INS surfaces and atmospheres, ideally complementing spectral observations (Taverna et al. 2014; González Caniulef et al. 2016).

2016AandA...594A..64P__Judge_(2015)_Instance_1

There is now renewed interest in the literature concerning these transitions, because some of the O iv and S iv intercombination lines, together with the Si iv resonance lines, are routinely observed with the Interface Region Imaging Spectrograph (IRIS; De Pontieu et al. 2014) at much higher spectral, spatial and temporal resolution than previously. For example, Peter et al. (2014) used the intensities of the O iv vs. Si iv lines to propose that very high densities, on the order of 1013 cm-3 or higher, are present in the so-called IRIS plasma “bombs”. Line ratios involving an O iv forbidden transition and a Si iv allowed transition have been used in the past to provide electron densities during solar flares and transient brightenings (e.g. Cheng et al. 1981; Hanssen 1981). However, the validity of using O iv to Si iv ratios has been hotly debated because these ratios gave very high densities compared to the more reliable ones obtained from the O iv ratios alone (see, e.g. Hayes & Shine 1987). In addition, Judge (2015) recalled several issues that should be taken into account when considering the Si iv/O iv density diagnostic. The main ones were: (1) O iv and Si iv ions are formed at quite different temperatures in equilibrium and hence a change in the O iv/Si iv ratio could imply a change in the temperature rather than in the plasma density (2) the chemical abundances of O and Si are not known with any great accuracy and could be varying during the observed events (3) density effects on the ion populations could increase the Si iv/O iv relative intensities by a factor of roughly three to four. Judge (2015) has also mentioned the well known problem of the “anomalous ions”, that is, the observed high intensities of the Li- and Na-like (as Si iv) ions (see also Del Zanna et al. 2002). Another important aspect to take into account is the effect of non-equilibrium conditions on the observed plasma diagnostics. It is well known that strong variations in the line intensities are obtained when non-equilibrium ionisation is included in the numerical calculations (see, e.g. Shen et al. 2013; Raymond & Dupree 1978; Mewe & Schrijver 1980; Bradshaw et al. 2004). In particular, Doyle et al. (2013) and Olluri et al. (2013) investigated the consequences of time-dependent ionization on the formation of the O iv and Si iv transition region lines observed by IRIS. In addition, Dudík et al. (2014) showed that non-Maxwellian electron distributions in the plasma can substantially affect the formation temperatures and intensity ratios of the IRIS Si iv and O iv lines. These authors also suggested that the observing window used by IRIS should be extended to include S iv. Recent IRIS observation sequences have indeed included the S iv line near 1406 Å. The S iv line ratios have a higher limit for density sensitivity than the O iv line ratios and are thus particularly useful for diagnosing high densities which might occur in flares. Previous flare studies have in fact reported line ratios involving O ions which lay above the density sensitivity range, indicating an electron density in the excess of 1012 cm-3 (e.g. Cook et al. 1995; Polito et al. 2016).

2016MNRAS.458.3655V__Casella,_Belloni_&_Stella_2005_Instance_1

Accreting stellar-mass black holes in binary systems regularly display quasi-periodic oscillations (QPOs) in their X-ray flux with frequencies drifting from ∼0.1–10 Hz (e.g. Van der Klis 1989). Three main components can be identified in the spectrum of these sources: disc blackbody emission, power-law emission from the inner accretion flow, and a reflection spectrum from photons reflected off the disc (Done, Gierlinski & Kubota 2007). So-called Type-C low-frequency QPOs (Casella, Belloni & Stella 2005) are believed to originate from the inner accretion flow/corona that is associated with the Comptonized power-law component of the X-ray spectrum, as this component shows a much larger variability amplitude than the blackbody disc component (Sobolewska & Zycki 2006; Axelsson, Hjalmarsdotter & C. 2013). Since currently no consensus on the origin of QPOs exists, we can generally divide QPO models into two broad categories: geometric and intrinsic models. In the former, the X-ray emission is constant but an oscillating accretion geometry quasi-periodically alters the observed flux. A possible origin for these geometric oscillations could be Lense–Thirring precession of the Comptonizing medium, due to misalignment of the black hole spin and the binary orbit (Stella & Vietri 1997; Stella, Vietri & Morsink 1999; Ingram, Done & Fragile 2009). Alternatively, in intrinsic models the emitted luminosity itself varies, for example due to changes in mass accretion rate (Tagger & Pellat 1999; Cabanac et al. 2010) or due to a standing shock in the accretion flow (Chakrabarti & Molteni 1993). Recently, Heil, Uttley & Klein-Wolt (2015) and Motta et al. (2015) confirmed that the QPO amplitude depends on the inclination of the binary orbit, strongly suggesting a geometric origin (Schnittman, Homan & Miller 2006). Ingram & Van der Klis (2015) found that the iron line equivalent width changes over a QPO cycle in GRS 1915+105, also strongly pointing towards a geometric origin.

2020ApJ...894..121M__Bruno_&_Carbone_2013_Instance_1

Current sheet drift is implemented following the approach proposed by Burger (2012) to calculate the drift velocity Vd in Equation (7). The heliospheric current sheet angle is modeled as by Kóta & Jokipii (1983), but now with a time-dependent tilt angle from Equation (8) such that 12 where, after Burger et al. (2008), we employ 13 with ϕ0 = 0°, thereby allowing for the inclusion in the model of the effects of a fully time-dependent heliospheric current sheet. Diffusion coefficients are modeled as by Moloto et al. (2018). A composite slab/2D model for transverse magnetostatic turbulence is assumed (see, e.g., Matthaeus et al. 1995), as well as slab/2D turbulence power spectra with wavenumber-independent energy-containing ranges, and Kolmogorov inertial ranges. This latter assumption does not perfectly reflect spacecraft observations of the same (see, e.g., Bieber et al. 1993; Matthaeus et al. 2007; Bruno & Carbone 2013), but leads to relatively simple, tractable expressions for the parallel and perpendicular MFPs. For more detail on diffusion coefficients derived assuming more realistic forms for the turbulence power spectra, see Shalchi et al. (2010), Engelbrecht & Burger (2015b), and Strauss et al. (2016). In brief, then, the parallel MFP expression used here is that constructed by Burger et al. (2008) from the quasilinear theory results presented by Teufel & Schlickeiser (2003), and given by 14 where s = 5/3, R = RLkm, and km = 1/λsl the wavenumber at which the slab spectrum inertial range commences, and RL is the maximal proton Larmor radius. To model the perpendicular MFP, we use the Nonlinear Guiding Center result of Shalchi et al. (2004), as modified by Burger et al. (2008) to take into account a general ratio of slab to 2D energies (see, e.g., Bieber et al. 1994, 1996). This expression is similar to that derived by Zank et al. (2004), and is given by 15 where ν = 5/6 denotes half the Kolmogorov inertial range spectral index, and we assume that α2 = 1/3 (Matthaeus et al. 2003). Drift coefficients, reduced in the presence of turbulence, are modeled following the approach of Engelbrecht et al. (2017), so that the lengthscale corresponding to the drift coefficient is given by 16 with being the total magnetic variance. This expression is chosen as it provides results in reasonable agreement with numerical test particle simulations done by Minnie et al. (2007) and Tautz & Shalchi (2012) for the range of turbulence conditions expected in the supersonic solar wind, and due to the fact that its use in a CR modulation code has been shown by Moloto et al. (2018) to lead to computed CR intensities in reasonable agreement with spacecraft observations.

2022MNRAS.515L..39Z__Weiss_et_al._2008_Instance_1

Rocky planets are the only harbour for life form in the Solar system, so unravelling their origin and history is fundamental for understanding the habitability of planets other than Earth (Lineweaver & Chopra 2012; Cockell et al. 2016). For example, mantle–crust differentiation on Earth has set boundary conditions through redox conditions and early degassing processes that liquid water can occur on the surface of Earth. Geochemical studies, mainly elemental and isotopic compositions, on the specimens from these planets provide significant information about differentiation processes and their timing. Sample-return missions represent one way to obtain these specimens from the differentiated planets (e.g. Anand et al. 2020), but at great time and expense. Non-chondrite meteorites (including achondrites) also originate from differentiated asteroids and planets, e.g. from Moon, Mars, and Vesta, and the angrite and ureilite parent bodies (Binzel & Xu 1993; Weiss et al. 2008; Agee et al. 2013; Marchi et al. 2013; Bischoff et al. 2014). Some non-chondritic meteorites have a unique mineralogy and bulk composition, indicative of core, mantle, and crustal domains of their parent bodies, and thus, these samples record large-scale early planetary differentiation events. For instance, ureilites (Mg-rich, dominated by olivine and pyroxene) and iron meteorites (Fe–Ni metal) are from the mantle and core of asteroids, respectively, and record planetary mantle differentiation and core formation (Goodrich, Scott & Fioretti 2004; Goldstein, Scott & Chabot 2009). In contrast, shergottite and howardite–eucrite–diogenite meteorites, which are inferred to have derived from Mars, 4 Vesta, and related bodies, reflect a variety of crustal compositions and processes (Mezger, Debaille & Kleine 2013; Mittlefehldt 2015). In addition to the well-known achondrite groups with numerous members, ungrouped achondrites, e.g. Northwest Africa (NWA) 011 (Yamaguchi et al. 2002), Graves Nunatak (GRA) 06128/06129 (Day et al. 2009), NWA 11119 (Srinivasan et al. 2018), and NWA 7325 (Koefoed et al. 2016) expand the compositional range of achondrites towards chemically more evolved compositions (e.g. higher SiO2 contents), and thus, showcase the diversity of planetary and asteroidal crusts in the Solar system. Some achondrites, e.g. NWA 11119 and NWA 7325, yield evidence for their accretion and differentiation within the first ∼5 Myr after the formation of Ca–Al-rich inclusions (CAIs); (Koefoed et al. 2016; Srinivasan et al. 2018; Zhu et al. 2019b; Barrat et al. 2021). Hence, dating more achondrites is beneficial to map the early history of Solar system.

2018AandA...613A..35K__Anderson_et_al._2010_Instance_1

As shown in Fig. 3, the differences in metallicity between different SN subclasses are not significant. This is in contradiction with what is expected from single-star evolution theory, where metallicity-driven winds are crucial: type Ic SNe, which are the most highly stripped, would show the highest metallicity, followed by type Ib, and finally the H-rich type II SNe. The observations, on the other hand, reveals that this is not the case. Some SNe Ic are even located in the low-metallicity part of the distribution in the current sample. This result strengthens the notion that metallicity may not play an important role in deciding theresulting SN type, in agreement with other works based on SN environments (Anderson et al. 2010, 2015; Leloudas et al. 2011; Galbany et al. 2016a). The environments of broad-lined SNe IcBL are found to be relatively metal poor compared to the normal CCSNe, in agreement with previous studies (Modjaz et al. 2011; Galbany et al. 2016a). However, we note that there are only two such SNe in the current sample. The explosion site of SN 1998bw (the first SN to be associated with a GRB: 980425; Galama et al. 1998; Krühler et al. 2017) in this study shows a lower metallicity of 12 + log(O/H) = 8.30 dex compared to the GRB-less SN 2009bb (Pignata et al. 2011), 12 + log(O/H) = 8.49 dex. Levesque et al. (2010a), using slit spectroscopy of the explosion site, concluded that the high metallicity of SN 2009bb site is consistent with typical GRB-less SNe IcBL and not with GRB hosts. Their metallicity value recalculated on the Marino et al. (2013) N2 scale is 12 + log(O/H) = 8.52 dex. These two different cases illustrate the importance of metallicity in deciding whether an SN IcBL progenitor would also produce GRB or not (Modjaz et al. 2008; Levesque et al. 2010b). Progenitors with higher metallicity are not able to spin fast enough and thus produce high angular momentum essential for GRB jet production, eventually producing a GRB-less SN IcBL (Woosley & Bloom 2006).

2021MNRAS.503..354G__Griv_et_al._2020_Instance_1

The Lindblad–Oort idea of a rotation of the Galaxy around the Galactic Centre (GC) proposes that for any type of Galactic object there is at each point in the plane a mean circular, differential motion (Oort 1927a,b). In the conventional lowest order approach to the determination of the rotation parameters of the system, a strictly circular model is adopted (Mihalas & Binney 1998). Small non-axisymmetric spiral-like perturbations of the basic axisymmetric gravitational potential induce non-circular variations of stellar velocities proportional to $\widetilde{v}_r \cos \phi$ and $\widetilde{v}_\varphi \sin \phi$, and we are looking for these systematic motions in our study. Here, $\widetilde{v}_r$ and $\widetilde{v}_\varphi$ are the amplitudes of the radial and tangential wave motions that depend weakly on the Galactocentric distance r, and ϕ is the phase of the wave (Lin et al. 1969; Yuan 1969; Rohlfs 1977; Griv et al. 2020, annexure B therein). The method of analysis used for this purpose follows the development of Crézé & Mennessier (1973), Byl & Ovenden (1978), Mishurov, Pavlovskaya & Suchkov (1979), and Grivnev (1981). Specifically, in addition to the ordinary circular rotation of the system that is axisymmetric in the mean, Galactic objects have small streaming wave motions. The radial and tangential components of the velocity of a star in the plane are represented as (1)$$\begin{eqnarray*} V_r = \widetilde{v}_r \cos \phi , \end{eqnarray*}$$(2)$$\begin{eqnarray*} V_\varphi = r \Omega + \widetilde{v}_\varphi \sin \phi , \end{eqnarray*}$$where Ω(r) is the angular velocity of the mean motion at the star’s distance r from the GC for the type of object considered, and the amplitudes $\widetilde{v}_r$, $\widetilde{v}_\varphi$ and the phase ϕ are to be found. Distinct populations of Galactic objects will have different average velocities Vφ at r, so the value of Ω should vary from population to population. An observed value of heliocentric line-of-sight velocity of a star $v$los corrected for solar motion towards the apex may be modeled in the following form: (3)$$\begin{eqnarray*} v_{\mathrm{los}} &=& \left\lbrace r_0 \left[ \Omega - \Omega _0 \right] \sin \ell - \widetilde{v}_r \cos \phi \cos (\ell +\varphi) \right. \nonumber \\ &&\left. \quad + \widetilde{v}_\varphi \sin \phi \sin (\ell +\varphi) + u_0 \cos \ell - v_0 \sin \ell \right\rbrace \cos b \nonumber \\ &&\quad - w_0 \sin b , \end{eqnarray*}$$where ℓ and b are the Galactic coordinates, the angle φ is measured clockwise in the direction of overall rotation from the radius passing through the location of the Sun, Ω0(r0) is the angular velocity of the mean motion at the Sun’s distance r0, ϕ = ϕ0 − m[φ − (1/tan p)ln (r/r0)], ϕ0 is the phase of the wave at the Sun’s location of the different Fourier m-modes, and the constants u0, $v$0, $w$0 are the components of solar peculiar motion relative to the mean linear speed of rotation at the Sun’s distance r0Ω0 (Mihalas & Binney 1998). To reiterate, the terms ∝(Ω − Ω0) and u0, $v$0, $w$0 describe the mean rotation of the system under study and the peculiar motion of the Sun. The deviation of the motion of objects from the circular motion due to a wave perturbation is characterized by terms ${\propto} \widetilde{v}_r \cos \phi$ and $\widetilde{v}_\varphi \sin \phi$.

2015ApJ...806..152S__Ransom_et_al._2005_Instance_2

One of the most astonishing characteristics of Liller 1 is the extremely large value of the collision rate parameter. Verbunt & Hut (1987) showed that Liller 1 has the second-highest value of stellar encounter rate (after Terzan 5; see also Lanzoni et al. 2010) among all star clusters in the Galaxy, thus suggesting that it represents an ideal environment where exotic objects, generated by collisions, can form. In fact, it is commonly believed that dynamical interactions in GCs facilitate the formation of close binary systems and exotic objects such as cataclysmic variables (CVs), low-mass X-ray binaries (LMXBs), millisecond pulsars (MSPs), and blue straggler stars (BSSs) (e.g., Bailyn 1992; Paresce et al. 1992; Ferraro et al. 2001, 2009a, 2012; Ransom et al. 2005; Pooley & Hut 2006). Moreover, Hui et al. (2010) found that clusters with large collisional parameters and high metallicity (see also Bellazzini et al. 1995) usually host more MSPs. Indeed, Terzan 5 hosts the largest population of MSPs among all Galactic GCs (Ransom et al. 2005). 6 6 Note that Terzan 5 is suspected to not be a genuine GC, because it harbors at least three stellar populations with different iron abundances (Ferraro et al. 2009a; Origlia et al. 2011, 2013; Massari et al. 2014). A strong γ-ray emission has been recently detected in the direction of Liller 1 by the Large Area Telescope (LAT) on board Fermi (Tam et al. 2011). This is the most intense emission detected so far from a Galactic GC, again suggesting the presence of a large number of MSPs. However, no direct radio detection of these objects has been obtained so far in this system (Ransom et al. 2005). The only exotic object identified in the cluster is the rapid burster MXB 1730-335, an LMXB observed to emit radio waves and type I and type II X-ray bursts (Hoffman et al. 1978). It seems to be located in the central region of Liller 1, but no optical/IR counterpart of this object has been found so far (Homer et al. 2001).

2021ApJ...910...52C__Kleihaus_et_al._2004_Instance_1

Half a century ago, a series of theorems laid the ground for the Kerr hypothesis (Israel 1967; Carter 1971; Robinson 1975); according to these no-hair theorems, the only stationary, axisymmetric, asymptotically flat, regular outside of the horizon solution to four-dimensional GR when the matter fields feature the same isometries as the spacetime is the Kerr BH. Notwithstanding their significance, there are many ways with which to circumvent them and discover different solutions. Still, in four dimensions, hairy BHs have been described in different theories of gravity, such as Einstein–Yang–Mills (Bizon 1990; Künzle & Masood-ul-Alam 1990; Volkov & Galtsov 1990; Breitenlohner et al. 1992; Kleihaus & Kunz 1998, 2001; Kleihaus et al. 2004), scalar-tensor (Bocharova et al. 1970; Bekenstein 1974; Kleihaus et al. 2015; Collodel et al. 2020a), and Gauss–Bonnet theories (Kanti et al. 1996; Kleihaus et al. 2011, 2016; Antoniou et al. 2018; Doneva & Yazadjiev 2018; Silva et al. 2018; Cunha et al. 2019; Collodel et al. 2020b; Herdeiro et al. 2021; Berti et al. 2021). Remarkably, by dropping the assumption that the matter fields must be stationary and axisymmetric, Herdeiro and Radu found solutions in the context of GR where BHs have hair (Herdeiro & Radu 2014b, 2015), by minimally coupling to gravity a complex scalar field that depends on time and on the axial coordinate while its energy-momentum tensor still possesses the respective isometries; see Herdeiro et al. (2015, 2016a, 2016b), Brihaye et al. (2016), and Delgado et al. (2016) for generalizations. These are known as scalarized Kerr black holes (KBHsSH) and they are the object of study of this paper. In their domain of existence, they connect Kerr BHs (that is, with no hair) with pure solitonic solutions, also known as boson stars (BS), which are regular everywhere and feature no horizons. In this sense, one can think of the KBHsSH indeed as a combined system of a BS with a horizon at its center, and therefore it shares traits of both objects.

2018MNRAS.476.1889T__Kulkarni,_Hut_&_McMillan_1993_Instance_1

The high interaction rates in globular clusters, especially at their centres, lead to the efficient formation of exotic binaries like accreting compact objects – cataclysmic variables (white dwarfs) and X-ray binaries (neutron stars and black holes; Clark, Markert & Li 1975). Soon after the first X-ray missions, it was recognized that X-ray transients in globular clusters were disproportionately associated with neutron stars, with no confirmed black holes (Verbunt et al. 1995), a fact that remains true to this day (Bahramian et al. 2014). In contrast, about a third of the X-ray transients in the rest of the Galaxy contain black holes (Miller-Jones et al. 2015; Tetarenko et al. 2016). Originally it was proposed that due to mutual gravitational interactions that all black holes would have been ejected from globular clusters (Kulkarni, Hut & McMillan 1993; Sigurdsson & Hernquist 1993). However, there are several recent indications that a number of black holes may still exist within clusters, based on X-ray observations of extragalactic clusters (Maccarone et al. 2007; Brassington et al. 2010; Irwin et al. 2010; Shih et al. 2010; Maccarone et al. 2011), radio/X-ray observations of Galactic clusters (Maccarone & Knigge 2007; Strader et al. 2012; Chomiuk et al. 2013; Miller-Jones et al. 2015), as well as theoretical simulations (Sippel & Hurley 2013; Morscher et al. 2015; Peuten et al. 2016). The reason why these black holes are so elusive could perhaps be due to the nature of the X-ray binaries they form, rather than their number. If the majority of accreting black holes in globular clusters have very low-mass, possibly degenerate, donors, they will have short, faint outbursts, making them undetectable by current and past all-sky X-ray surveys (Knevitt et al. 2014). Indeed, the dynamics in globular clusters are thought to effectively produce ultracompact X-ray binaries (Verbunt 1987; Deutsch et al. 1996; Deutsch, Margon & Anderson 2000; Ivanova et al. 2005, 2010), in which a black hole or neutron star accretes matter from an H-poor donor in a system with a very short orbital period (≲1  h; Nelson, Rappaport & Joss 1986).

2020AandA...644A..97C__Leroy_et_al._2013_Instance_4

Major nearby galaxy cold gas mapping surveys (Regan et al. 2001; Wilson et al. 2009; Rahman et al. 2011; Leroy et al. 2009; Donovan Meyer et al. 2013; Bolatto et al. 2017; Sorai et al. 2019; Sun et al. 2018) have focused on observations of the molecular gas (through CO lines). Despite a few notable exceptions (e.g. Alatalo et al. 2013; Saintonge et al. 2017), these surveys observed mainly spiral or infrared-bright galaxies (i.e. galaxies with significant star formation) and have furthered our understanding of how star formation happens, rather than how it stops. This boils down to quantifying the relation between molecular gas and star formation rate (SFR), which appears nearly linear in nearby discs (Kennicutt 1998; Bigiel et al. 2008; Leroy et al. 2013; Lin et al. 2019). This relationship is often parametrised via the ratio between the SFR and the molecular gas mass (Mmol), which is called the molecular star formation efficiency (SFE = SFR/Mmol = 1∕τdep), where the inverse of the SFE is the depletion time, τdep. The depletion time indicates how much time is necessary to convert all the available molecular gas into stars at the current star formation rate. On kpc scales and in the discs of nearby star-forming galaxies, τdep is approximately constant around 1–2 Gyr (Bigiel et al. 2011; Rahman et al. 2012; Leroy et al. 2013; Utomo et al. 2017), and it appears to weakly correlate with many galactic properties such as stellar mass surface density or environmental hydrostatic pressure (Leroy et al. 2008; Rahman et al. 2012). Nevertheless, small but important deviations for a constant SFE have been noticed, which can be the first hints of star formation quenching. In some galaxies, the depletion time in the centres appear shorter (Leroy et al. 2013; Utomo et al. 2017) or longer (Utomo et al. 2017) with respectto their discs. These differences may correlate with the presence of a bar or with galaxy mergers (Utomo et al. 2017; see also Muraoka et al. 2019) and do not seem to be related to unaccounted variation in the CO-to-H2 conversion factor (Leroy et al. 2013; Utomo et al. 2017). Spiral arm streaming motions have also been observed to lengthen depletion times (Meidt et al. 2013; Leroy et al. 2015).

2020AandA...637A..59A__Massalkhi_et_al._2019_Instance_3

Silicon monoxide (SiO) is predicted to be the most abundant Si-bearing molecule in the entire 1–10 R* range in the atmospheres of M stars. In S-type atmospheres, the calculated abundance of SiO decreases by two orders of magnitude in the 1–5 R* but retains a very high abundance beyond, and the same occurs in C-rich atmospheres, although in this case, the abundance drop in the 1–5 R* is even more pronounced (see Fig. 2; see also Agúndez & Cernicharo 2006). Observations indicate that the abundance of SiO does not differ significantly between envelopes around M-, S-, and C-type stars, although in all them the SiO abundance decreases with increasing mass-loss rate (González Delgado et al. 2003; Schöier et al. 2006; Ramstedt et al. 2009; Massalkhi et al. 2019, 2020). This decline in the SiO abundance with increasing envelope density is not a consequence of chemical equilibrium (Massalkhi et al. 2019), but has been interpreted as evidence that SiO disappears from the gas phase at high densities to be incorporated into dust grains (González Delgado et al. 2003; Schöier et al. 2006; Ramstedt et al. 2009; Massalkhi et al. 2019, 2020). It therefore appears that the gradual abundance decline calculated for SiO in the 1–5 R* region from stellar type in the sense M → S → C does not have a direct consequence in the SiO abundance that is injected into the expanding wind. However, this behavior predicted by chemical equilibrium probably explains why SiO masers are observed in M-type stars but not toward carbon stars (e.g., Pardo et al. 2004). Except for these details, chemical equilibrium and observations agree in the fact that SiO is one of the most abundant carriers of silicon in the atmospheres of M-, S-, and C-type stars. Calculations and observations also agree for SiS in that it is an abundant molecule regardless of the C/O. However, observations indicate a differentiation between C- and O-rich envelopes, with SiS being on average one order of magnitude more abundant in carbon-rich sources (Schöier et al. 2007; Danilovich et al. 2018; Massalkhi et al. 2019, 2020). Moreover, in some oxygen-rich envelopes, the fractional abundance of SiS relative to H2 is as low as ~10−8, which is well below the predictions of chemical equilibrium (Danilovich et al. 2019; Massalkhi et al. 2020).

2021ApJ...916...70Z__Liu_et_al._2020_Instance_1

At this juncture in cosmology, independent and complementary probes with considerable precision are very helpful for providing clues about the origin of the abovementioned crises. Strong-lensing systems are one of the most promising probes for investigating these issues. The time delay between images of strongly lensed time-variable sources was proposed to directly determine H0 (Refsdal 1964; Treu 2010). Recently, with elaborate time-delay measurements and lens modeling for five selected strongly lensed quasar systems, the H0 Lenses in COSMOGRAIL’s Wellspring (H0LiCOW) team yielded an estimation of H0 that is also 2.3σ higher than the Planck-calibrated value (Birrer et al. 2019). It should be mentioned that this result is completely independent of all rungs of the distance ladder. Moreover, on the basis of the distance sum rule (DSR), distance ratios derived from angular separation measurements of images in strong-lensing systems were proposed to test the FLRW metric and model-independently determine ΩK (Räsänen et al. 2015). This method has been intensively implemented with updated observations (Xia et al. 2017; Li et al. 2018a; Liu et al. 2020; Zhou & Li 2020). In order to reduce the systematics resulting from oversimplified lensing modeling in the distance ratio method, Liao et al. (2017b) reformulated the DSR in terms of the time-delay distance, which is a combination of three angular diameter distances in a strong-lensing system. With the latest strongly lensed quasar observations and type Ia supernovae (SNe Ia) luminosity distance measurements, H0 and ΩK were simultaneously and model-independently determined to a precision of ∼6% and ∼0.3 (Collett et al. 2019), respectively. Meanwhile, strongly lensed gravitational waves (GWs) and their electromagnetic (EM) counterparts from the binary of compact object coalescence are proposed as a powerful tool for precision cosmology since the time delay between images in these systems can be precisely measured (Liao et al. 2017a; Li et al. 2019). However, both traditional strongly lensed quasar and proposed strongly lensed GW systems face shortages. For lensed quasars, the precision of time-delay measurements is limited to ∼3% and lens modeling is difficult to improve because of the bright active-galactic nuclei (AGNs) contamination in the source host galaxy. For the expected lensed GWs, the main challenges may be the event rate and localization ability for images of GW signals, which is crucial for lens modeling.

2015AandA...579A..56B__Lebrun_et_al._2003_Instance_1

Swift J1734.5-3027 was detected by INTEGRAL for the first time during the observations performed toward the Galactic bulge in the satellite revolution 1329, i.e., about half a day before the BAT discovery (from 56 535.85950 MJD to 56 536.01338 MJD; see Table 1). It remained within the field of view (FoV) of the instruments on-board INTEGRAL until satellite revolution 1348 (from 56 592.54664 MJD to 56 593.31612 MJD), when the window of seasonal visibility toward the Galactic center closed. We analyzed all INTEGRAL data by using version 10.1 of the Off-line Scientific Analysis software (OSA) distributed by the ISDC (Courvoisier et al. 2003). INTEGRAL observations are divided into science windows (SCWs), i.e., pointings with typical durations of ~2−3 ks. To limit the ISGRI calibration uncertainties (Lebrun et al. 2003), we made use of all public SCWs from the Galactic bulge, Scutum/Sagittarium, and Perseus/Norma monitoring programs during which the source was located to within 12 deg from the center of the IBIS FoV (Ubertini et al. 2003). We also included in our dataset the observations of the Galactic center and 4U 1728-34 for which our group was awarded data rights in revolutions 1329−1348. A summary of the total exposure-time available in each revolution is provided in Table 1 for IBIS/ISGRI and the two JEM-X telescopes (Lund et al. 2003). We extracted the mosaics and spectra in each revolution for the two JEM-X and ISGRI. All JEM-X spectra were extracted by using the standard 16-channel response matrix, while a customized 37 energy bin response matrix was created for ISGRI in order to optimize the signal-to-noise ratio (S/N) in the energy range (20−50 keV). Only in revolution 1329, was the ISGRI spectrum extracted with a reduced energy binning (8 channels), as the source was relatively faint (see Sect. 3). JEM-X lightcurves with a time resolution of 2 s were extracted from all observations to search for Type-I X-ray bursts, but none was found. We did not perform other timing analyses of the INTEGRAL data as the source was too faint to extract meaningful power spectra. In Fig. 1 we show the ISGRI mosaic realized by using all available data.

2020AandA...641A..85S__Orienti_&_Dallacasa_2008_Instance_2

To derive the equipartition magnetic field of J1146+4037, we predict the rest-frame 8.4 GHz (redshifted to 1.4 GHz at z = 5.0059) flux density from our spectral model. However, there is no source size measurement at 1.4 GHz. We make use of the full width at half maximum (FWHM) source size of 0.74 ± 0.01 mas derived by the Gaussian fit from 5 GHz VLBI mas angular resolution observations (Frey et al. 2010). We note that in our calculations, we assume a source size that is 1.8 times larger than the FWHM, following the approach of Readhead (1994) and Orienti & Dallacasa (2008). The derived equipartition magnetic field is 34 − 7 + 8 $ 34^{+8}_{-7} $ mG. This is within the range of the equipartition magnetic fields of 17 HFP radio sources (7–60 mG; quasars and galaxies at 0.22   z   2.91; Orienti & Dallacasa 2012) and 5 HFPs at 0.084   z   1.887 (18–160 mG; Orienti & Dallacasa 2008). The magnetic field calculated from the turnover information listed in Table 4 is 1 . 8 − 2.7 + 2.3 $ 1.8^{+2.3}_{-2.7} $ G assuming an SSA origin with Eq. (3), however the uncertainty is very large. The large uncertainty is caused by the fact that we only have four data points to constrain the turnover information and we do not have source size measurements at the turnover frequency, but rather we assume the source size measured at another frequency. More data taken in other wavelength bands are needed to meaningfully constrain the turnover peak, and mas resolution observations at the peak frequency are needed to give reliable magnetic field strength measurements. This may indicate that the turnover is not caused by SSA, by comparing the large magnetic field strength measured from the spectral turnover ( 1 . 8 − 2.7 + 2.3 $ 1.8^{+2.3}_{-2.7} $ G) with the equipartition magnetic field strength ( 34 − 7 + 8 $ 34^{+8}_{-7} $ mG). As J1146+4037 is a strong blazar, the turnover may be caused by its strong variability. Another possible explanation for the spectral turnover is that high-density plasma in the nuclear region attenuates the radio emission from the central active BH. High-resolution, interstellar medium observations of the nuclear region of this target may address the latter issue.

2021AandA...654A..89P__Vincentelli_et_al._2021_Instance_1

The spectral analysis above 3 keV allows us to characterise the hot corona and the relativistic reflection emission properties. The deep January 2017 NuSTAR observation was also included in the simultaneous fit (Ezhikode et al. 2020; Panagiotou & Walter 2020). From the RELXILL reflection model, and assuming a primary exponential cutoff power-law continuum, we find moderate reflection strengths, ℛ ∼ 0.1−0.2, and high cutoff energies, Ecut ∼ 110−120 keV. These values are in very good agreement with those measured from the average long-term Swift/BAT (Burst Alert Telescope) spectrum (Vincentelli et al. 2021). Applying relativistic reflection models that assume a primary Comptonisation continuum, we infer the hot corona temperature to be kThot ∼ 26−31 keV (kThot ∼ 21−22 keV) and the optical depth to be τhot ∼ 2 (τhot ∼ 6−7) for the slab (or spherical) geometry. From the spectral analysis, it is not possible to discriminate between either of the hot corona geometries, although the slab geometry provides a better fit. In the near future, X-ray polarimetry is expected to play an important role within such a framework (e.g. Schnittman & Krolik 2010; Beheshtipour et al. 2017; Tamborra et al. 2018; Marinucci et al. 2019) thanks to, for example, IXPE (Imaging X-Ray Polarimetry Explorer; Weisskopf et al. 2016) and eXTP (Enhanced X-ray Timing and Polarimetry observatory; Zhang et al. 2016). While the corona temperatures found for Mrk 110 are broadly consistent with the average ones found by Middei et al. (2019) from a sample of 26 AGN (with ⟨kThot⟩ = 50 ± 21 keV and ⟨kThot⟩ = 53 ± 23 keV for the slab and spherical geometry, respectively), it is likely to be located in the lower range of this distribution. However, its hot coronal temperature is not as low as the temperatures inferred for some AGN with much lower high-energy cutoffs, such as GRS 1734–292 (Tortosa et al. 2017), Ark 564 (Kara et al. 2017), and PDS 456 (Reeves et al. 2021b), where kT could be as low as 15 keV.

2020AandA...639A.104S__Kitaura_et_al._2006_Instance_1

Today, at least three known populations of gap transients are recognized in the luminoisty gap. These include classical luminous blue variable (LBV) outbursts, intermediate-luminosity red transients (ILRTs), and luminous red novae (LRNe). LBVs are thought to be related to eruptions of massive luminous stars (see Smith et al. 2011). As shown in greater detail in Stritzinger et al. (2020, hereafter Paper I), the ILRT subtype is well represented by NGC 300-2008-OT and SN 2008S, and has been linked to asymptotic giant branch (S-AGB) stars (Prieto et al. 2008, 2009; Thompson et al. 2009; Botticella et al. 2009; Kochanek 2011; Adams et al. 2016; Doherty et al. 2017) that die as electron-capture supernovae (Miyaji et al. 1980; Nomoto 1984; Miyaji & Nomoto 1987; Hashimoto et al. 1993; Kitaura et al. 2006; Poelarends et al. 2008). Other models appearing in the literature for ILRTs consist of moderately massive stars experiencing super-Eddington winds and/or giant outbursts (e.g., Smith et al. 2009; Humphreys et al. 2011), or massive stars donating material to a main-sequence star, leading to the release of gravitational energy (e.g., Kashi et al. 2010). Finally, a leading model for the origins of LRNe, which all display a ubiquitous double-humped light curve (Pastorello et al. 2019a), consists of the ejection of a common envelope by a massive binary system (e.g., Blagorodnova et al. 2017) upon coalescence (Smith et al. 2016; Metzger & Pejcha 2017; Lipunov et al. 2017; Mauerhan et al. 2018). However, other models have also been proposed in the past to account for LRNe, particularly within articles that have studied the Galactic LRN archetype V838 Mon. These include, among others, outbursts from massive stars (Tylenda 2005), accretion of low-mass stars onto solar-mass main-sequence companions (Soker & Tylenda 2003; Tylenda & Soker 2006; Kashi et al. 2010; Kashi & Soker 2016; Soker 2020), or even giant stars that accrete relatively massive planets (Retter & Marom 2003).

2021ApJ...911...89M__Mozer_et_al._2020a_Instance_2

Time domain structures (TDSs; electrostatic or electromagnetic electron holes, ion holes, solitary waves, double layers, nonlinear whistlers, etc.) are ∼1 ms pulses having significant electric fields parallel to the background magnetic field (Mozer et al. 2015). They are abundant through space, occurring along auroral zone magnetic field lines (Temerin et al. 1982; Mozer et al. 1997; Ergun et al. 1998), in the magnetospheric tail and plasma sheet (Cattell et al. 2005; Tong et al. 2018; Lotekar et al. 2020), at reconnection sites (Cattell et al. 2005; Steinvall et al. 2019; Lotekar et al. 2020), in the solar wind (Mangeney et al. 1999; Malaspina et al. 2013), in collisionless shocks (Wilson et al. 2010; Vasko et al. 2020; Wang et al. 2020), and in the magnetospheres of other planets (Pickett et al. 2015). TDSs are also expected along the Parker Solar Probe orbit (Mozer et al. 2020a). According to theoretical estimates and simulations (Cranmer & van Ballegooijen 2003; Valentini et al. 2011, 2014), these nonlinear structures can provide thermalization of electron and ion beams produced in the course of the turbulence cascade development at scales the order of the electron inertial length and down to the Debye length. This paper discusses such observations at a heliocentric distance of 35 solar radii. The electric field experiment on the Parker Solar Probe measures electric fields from DC to 20 MHz. A general description of the instrument and its electronics appears elsewhere (Bale et al. 2016). In this paper, the data from DC to 2 MHz are discussed. These measurements are obtained from the potentials of the four antennas, V1 through V4, that are located in the plane perpendicular to the Sun–satellite line. They produce E12 = (V1−V2)/3.5 and E34 = (V3−V4)/3.5, which are then rotated into the spacecraft coordinate system to produce EX and EY. The direction, X, is perpendicular to the Sun–spacecraft line, in the ecliptic plane, and pointing in the direction of solar rotation (against the ram direction), Y is perpendicular to the ecliptic plane, pointing southward, and Z points toward the Sun. The numerical factor, 3.5, is the effective antenna length (Mozer et al. 2020a). The uncertainties of the amplitudes of the waves and time domain structures reported in this paper are estimated to be about a factor of 2. These amplitudes are underestimated because the capacitive divider that couples the antennas to the electronics decreases the measured electric field relative to that on the antennas. They are often overestimated because short antennas produce overestimates of the electric field by factors of 2–4, as was observed during antenna deployment on the Cluster satellite and as is observed on the Parker Solar Probe from the ratio of the electric field to the magnetic field in whistlers.

2019AandA...629A..54U__Marinucci_et_al._2015_Instance_1

NGC 2110. NGC 2110 is another nearby (z = 0.00779, Gallimore et al. 1999), X-ray bright Seyfert galaxy. Diniz et al. (2015) report a black hole mass of 2 . 7 − 2.1 + 3.5 × 10 8 M ⊙ $ 2.7^{+ 3.5}_{- 2.1} \times 10^{8}\,{{M}_{\odot}} $ , from the relation with the stellar velocity dispersion. From BeppoSAX data, Malaguti et al. (1999) found the X-ray spectrum to be affected by complex absorption. This has been later confirmed by Evans et al. (2007), who find the Chandra+XMM–Newton data to be well fitted with a neutral, three-zone, partial-covering absorber. Rivers et al. (2014) find the Suzaku data to be well fitted with a stable full-covering absorber plus a variable partial-covering absorber. A soft excess below 1.5 keV is also present (Evans et al. 2007), and possibly due to extended circumnuclear emission seen with Chandra (Evans et al. 2006). No Compton reflection hump has been detected with Suzaku (Rivers et al. 2014) or NuSTAR (Marinucci et al. 2015), despite the presence of a complex Fe Kα line. According to the multi-epoch analysis of Marinucci et al. (2015), the Fe Kα line is likely the sum of a constant component (from distant, Compton-thick material) and a variable one (from Compton-thin material). Concerning the high-energy cut-off, ambiguous results have been reported in literature (see Table 1). Ricci et al. (2017) report a value of 448 − 55 + 63 $ 448^{+63}_{-55} $ keV, while Lubiński et al. (2016) report a coronal temperature of 230 − 57 + 51 $ 230^{+51}_{-57} $ keV and an optical depth of 0 . 52 − 0.13 + 0.14 $ 0.52^{+ 0.14}_{- 0.13} $ . From 2008–2009 INTEGRAL data, Beckmann & Do Cao (2010) report a cut-off of ∼80 keV with a hard photon index, but these results are not confirmed by NuSTAR (Marinucci et al. 2015). Indeed, only lower limits to the high-energy cut-off have been found with NuSTAR (210 keV: Marinucci et al. 2015), Suzaku (250 keV: Rivers et al. 2014) and BeppoSAX (143 keV: Risaliti 2002). No hard X-ray spectral variability has been detected by Caballero-Garcia et al. (2012) and Soldi et al. (2014) from BAT data, despite the significant flux variability.

2021AandA...651L...8D__Just_et_al._2007_Instance_2

As the most luminous persistent sources in the Universe, quasars are bright enough to be detected up to redshifts z > 7 (Mortlock et al. 2011; Banados et al. 2018; Wang et al. 2018; Yang et al. 2020). According to the currently accepted model, quasars are extremely luminous active galactic nuclei (AGNs), where the observed intense energy release are related to the accretion of a gaseous disk onto a supermassive black hole (SMBH). Quasars have a wide spectral energy distribution, which normally contains a significant emission component in the optical-UV band LUV, the so-called big blue bump, with a softening at higher energies (Sanders et al. 1989; Elvis et al. 1994; Trammell et al. 2007; Shang et al. 2011). It has long been discussed that there is a nonlinear relationship between LUV and the quasar’s X-ray luminosity LX, parametrized as log(LX) = γlog(LUV)+β (Vignali et al. 2003; Strateva et al. 2005; Steffen et al. 2006; Just et al. 2007; Green et al. 2009; Young et al. 2010; Jin et al. 2012). From the theoretical point of view, this relation could be intrinsic since the UV emission is usually thought to originate from the optically thick disk surrounding the SMBH and the X-ray photons are thought to be generated through the inverse-Compton scattering of these disk UV photons by a plasma of hot relativistic electrons (the so-called corona) around the accretion disk. Such a relation is found to be independent of redshift (Lusso & Risaliti 2016), so that it could be used as a distance indicator to estimate cosmological parameters. The initial dispersion of the LUV − LX relation is relatively large (δ ∼ 0.35−0.4, Just et al. 2007; Young et al. 2010), but after a detailed study, Lusso & Risaliti (2016) suggest that most of the observed dispersion is not intrinsic, but it is rather due to observational effects. By gradually refining the selection technique and flux measurements, Risaliti & Lusso (2019) collected a complete sample of quasars, whose dispersion of the LUV − LX relation is smaller than 0.15 dex. The sample of main quasars is composed of 1598 data points in the range from 0.036  z  5.1. With this sample, they constructed a Hubble diagram of quasars in redshift range of 0.5  z  5.5, which is in excellent agreement with the analogous Hubble diagram for SNIa in the redshift range of 0.5  z  1.4. Moreover, this Hubble diagram of quasars has been studied in cosmological applications (Zheng et al. 2020, 2021). Considering that objects at the same redshift should have the same luminosity distance in any cosmology, here we first fit the model-independent cosmography formula that reflects the Hubble relation between the luminosity distance and redshif using the quasar sample, and then we obtained the distance moduli (also the luminosity distance) for GRBs at a given redshift with the best fit results.

2017AandA...599A..55B__Shakura_1972_Instance_2

When the characteristic time of variability of the mass flux along the accretion disk is longer than the relaxation time of the local disk equilibrium, it is possible to use the approximation of local equilibrium Shakura (1972), see also Bisnovatyi-Kogan (2011), to calculate the transient disk structure. The equilibrium along a radius of the accretion disk around a star with a mass M is determined by the Keplerian rotational velocity ΩK(1)\begin{equation} \Omega=\Omega_{\rm K}=\left(\frac{GM}{r^3}\right)^{1/2}\cdot \label{omega} \end{equation}Ω=ΩK=GMr31/2·Writing the equation of the vertical equilibrium in approximate algebraic form, we obtain (2)\begin{equation} h=\sqrt 2 \frac{v_{\rm s}}{\Omega}, \label{h} \end{equation}h=2vsΩ,where \hbox{$v_{\rm s}=\sqrt{P/\rho}$}vs=P/ρ is the speed proportional to the sound velocity, P and ρ are the (gas + radiation) pressure and density at the symmetry plane of the accretion disk, and h is the semi-thickness of the accretion disk. The specific angular momentum l of the matter in the accretion disk is connected to the rotation velocity as(3)\begin{equation} l=r\,v_\phi=r^2\Omega. \label{l} \end{equation}l=r vφ=r2Ω.The mass flux through the disk at radius r is connected to the radial velocity vr as (4)\begin{equation} \dot M=-4\pi h\rho r v_r, \quad \dot M>0,\quad v_r<0. \label{mflux} \end{equation}Ṁ=−4πhρrvr, Ṁ>0, vr0.We use an α approximation for the turbulent viscosity (Shakura 1972) when the (rφ) component of the stress tensor trφ is written as (5)\begin{equation} t_{r\phi}=\alpha\, P, \label{trphi} \end{equation}trφ=α P,where the phenomenological non-dimensional parameter α ≤ 1. The condition of stationarity of the angular momentum, in which the outward viscous radial flux of the angular momentum is balanced by the angular momentum of the inward flux of the mass, is written as (see, e.g., Bisnovatyi-Kogan 2011) (6)\begin{equation} r^2 h \alpha P=\frac{\dot M}{4\pi}(l-l_{\rm in}). \label{angmom} \end{equation}r2hαP=Ṁ4π(l−lin).The main input into the time lag comes from the outer regions of the disk with l ≫ lin. Then we have from Eqs. (4) and (6) the expression for the radial velocity in the form (7)\begin{equation} v_r=-\alpha\frac{v_{\rm s}^2}{v_\phi}\cdot \label{vr} \end{equation}vr=−αvs2vφ·We also define the surface density Σ, and write Eq. (6) in light of Eq. (3), using condition l ≫ lin, in the form (8)\begin{equation} \Sigma=2\rho h,\quad \dot M \Omega=4\pi \alpha P h. \label{sigma} \end{equation}Σ=2ρh, ṀΩ=4παPh.The equation of the local thermal balance in the accretion disk, when the heat produced by viscosity Q+ is entirely emitted through the sites of the optically thick accretion disk with a total flux Q−, at l ≫ lin is written as (see, e.g., Bisnovatyi-Kogan 2011)(9)\begin{equation} \frac{3}{2}\dot M \Omega^2=\frac{16\pi ac T^4}{3\varkappa \Sigma}\cdot \label{theq} \end{equation}32ṀΩ2=16πacT43ϰΣ·Here T is the temperature in the symmetry plane of the accretion disk, a is the constant of the radiation energy density, c is the speed of light, and ϰ is the Thompson (scattering) opacity of the matter.

2022MNRAS.516.5618P__Muzahid_et_al._2020_Instance_1

Recent technological advances related to 3D Integral Field Spectroscopy (IFS), which produces data cubes where each pixel on the image has a spectrum, have opened a new window for examining the CGM gas. This approach combines the information gathered in absorption against background sources (whose lines of sight pass through a galaxy’s CGM) with traditional emission-based properties of galaxies. Following at least two decades of limited success in identifying the galaxies associated with quasar absorbers, IFS have open a new era in establishing the relation between absorption and emission with high success rates. Early efforts with near-infrared IFS VLT/SINFONI (Bouché et al. 2007; Péroux et al. 2011; Péroux et al. 2013, 2016) led to efficient discoveries of star-forming galaxies associated with Mg ii and H i absorbers at z ∼ 2 (see also Rudie, Newman & Murphy 2017; Joshi et al. 2021). The optical IFS VLT/MUSE (Bacon et al. 2010) has proved to be a true game-changer in the field. Early on, the MUSE Guaranteed Time Observations (GTO) team established surveys including MUSE-QuBES (Muzahid et al. 2020) and MEGAFLOW (Bouché et al. 2016; Schroetter et al. 2016; Schroetter et al. 2019; Zabl et al. 2019) to relate gas traced by absorbers to galaxies. In a parallel effort, the MAGG survey targets higher redshift galaxies (Fumagalli, O’Meara & Prochaska 2016; Dutta et al. 2020; Lofthouse et al. 2020). The Cosmic Ultraviolet Baryon Survey CUBS instead is absorption-blind and uncovers new quasar absorbers in a wide range of column densities (ranging from few times 16.0 $\rm{log}\,\,{\it N}(\rm{H\,\,{\small I}})$20.1) at z1 (Chen et al. 2020; Boettcher et al. 2021; Cooper et al. 2021; Zahedy et al. 2021). By extending to bluer wavelengths, the optical IFS Keck/KCWI (Martin et al. 2010) has enabled similar studies at higher spectral resolution (Martin et al. 2019; Nielsen et al. 2020). BlueMUSE, a blue-optimized, medium spectral resolution IFS based on the MUSE concept and proposed for the Very Large Telescope is also under planning (Richard et al. 2019). Contemporary to these works, ALMA - which can be viewed as an IFS at mm-wavelengths - has enabled the detections of both CO and [CII] emission in galaxies associated with strong quasar absorbers at intermediate and high redshifts, respectively (Neeleman et al. 2016; Kanekar et al. 2018; Klitsch et al. 2018; Neeleman et al. 2018; Neeleman et al. 2019; Péroux et al. 2019; Klitsch et al. 2021; Szakacs et al. 2021a). These lines enable us to trace the colder (∼100K) and denser phase of the neutral gas: the molecular hydrogen, H2. The molecular gas constitutes the ultimate phase of the gas reservoir from which stars form and hence is an essential link to the baryon cycle. Together, these IFS observations have provided unique information on the resolved galaxy kinematics which can then be combined with the gas dynamics to probe gas flows in the CGM regions (Bouché et al. 2013; Rahmani et al. 2018a; Schroetter et al. 2019; Zabl et al. 2019; Neeleman et al. 2020; Szakacs et al. 2021a).

2021MNRAS.507.1421M__Novikov,_Schmalzing_&_Mukhanov_2000_Instance_1

Topological estimators such as the Minkowski functionals (MFs) are also important diagnostics in this direction as they carry information at all orders. The MFs have been extensively developed as a statistical tool in a cosmological setting for both two-dimensional (2D; projected) and 3D (redshift) surveys. The MFs have analytically known results for a Gaussian random field, making them suitable for studies of non-Gaussianity. Examples of such studies include CMB data (Schmalzing & Górski 1998; Novikov, Schmalzing & Mukhanov 2000; Hikage et al. 2008; Natoli et al. 2010; Ducout et al. 2013; Planck Collaboration 2016b; Planck Collaboration IX, 2020a), LSS (Gott, Mellot & Dickinson 1986; Coles 1988; Gott et al. 1989, 1992; Melott 1990; Moore et al. 1992; Canavezes et al. 1998; Schmalzing & Diaferio 2000; Kerscher et al. 2001; Hikage et al. 2002, 2008 Park et al. 2005; Hikage, Komatsu & Mastubara 2006), weak lensing (Matsubara & Jain 2001; Sato et al. 2001; Taruya et al. 2002; Munshi et al. 2011d), Sunyaev–Zel’dovich maps (Munshi et al. 2011c), 21cm (Gleser et al. 2006), and N-body simulations (Schmalzing & Diaferio 2000; Kerscher et al. 2001). Note that this is an incomplete list of references and we have selected a sample of representative papers from the literature. The MFs are spatially defined topological statistics and, by definition, contain statistical information of all orders. This makes them complementary to the polyspectra methods that are defined in Fourier space. It is also possible that the two approaches will be sensitive to different aspects of non-Gaussianity and systematic effects, although in the weakly non-Gaussian limit it has been shown that the MFs reduce to a weighted probe of the bispectrum (Hikage et al. 2006). In addition to providing cosmological information, MFs can also be useful diagnostics of any unknown systematics as well as baryonic contamination which are expected to affect weak lensing observables (Herenois-Deraps et al. 2016).

2015ApJ...799..149J___2013_Instance_1

We use the microlensing magnification estimates for 27 quasar image pairs in 19 lens systems from MED09. In order to have the largest possible sample, but with a similar range of observed rest wavelengths, we include all of the objects from MED09 with magnifications measured in the wavelength range between Lyα (1216 Å) and Mg ii (2798 Å). With this choice, the average rest wavelength is λ = 1736 ± 373 Å, but we still keep 27 out of 29 image pairs from 19 out of 20 lensed quasars. Only the system RXS J1131−1231 is excluded, as it was observed in [O iii] at a much larger wavelength of ∼5000 Å. These microlensing magnification estimates are calculated after subtracting the emission line flux ratios, which are little affected by microlensing (see, e.g., Guerras et al. 2013), from the continuum flux ratios, and are therefore virtually free from extinction, substructure, and macro model effects (as these affect the line and continuum flux ratios equally). Our strategy is to compare the observed microlensing magnification for a given image pair with a statistical sample of simulated values for that measurement as a function of the source size (rs) and the fraction of surface mass density in stars (α). This will allow us to calculate the likelihood of the parameters (rs, α) given the observations . The procedure is repeated for each of the 27 image pairs. We calculate magnification maps for each image using a grid with 11 values for the fraction of the surface mass density in stars, α, logarithmically distributed between 0.025 and 0.8 as αj = 0.025 × 2j/2 with j = 0, …, 10. The 517 magnification maps were created using the Inverse Polygon Mapping algorithm described by Mediavilla et al. (2006, 2011a). We used equal mass microlenses of 1 M. All of the linear sizes can be scaled for a different microlens mass as . The maps have a size of 2000 × 2000 pixels with a pixel size of 0.5 light-days. The maps therefore span 1000 lt-days. The individual sizes of maps and pixels in (more natural) units of Einstein radii for microlenses of 1 M are given in Table 1. On average, the maps span approximately 50 Einstein radii with a pixel scale of roughly 0.025 Einstein radii.

2015MNRAS.448.2260G__Debes_et_al._2011_Instance_1

Furthermore, well-defined large samples of white dwarfs are an extremely useful starting point for identifying rare white dwarf types like magnetic white dwarfs (Gänsicke, Euchner & Jordan 2002; Schmidt et al. 2003; Külebi et al. 2009; Kepler et al. 2013), pulsating white dwarfs (Castanheira et al. 2004; Greiss et al. 2014), high/low-mass white dwarfs (Vennes & Kawka 2008; Brown et al. 2010; Hermes et al. 2014), white dwarfs with unresolved low-mass companions (Farihi, Becklin & Zuckerman 2005; Girven et al. 2011; Steele et al. 2013), white dwarfs with rare atmospheric composition (Schmidt et al. 1999; Dufour et al. 2010; Gänsicke et al. 2010), close white dwarf binaries (Marsh, Nelemans & Steeghs 2004; Parsons et al. 2011), metal polluted white dwarfs (Sion, Leckenby & Szkody 1990; Zuckerman & Reid 1998; Dufour et al. 2007; Koester, Gänsicke & Farihi 2014) or white dwarfs with dusty or gaseous planetary debris discs (Gänsicke et al. 2006; Farihi, Jura & Zuckerman 2009; Debes et al. 2011; Wilson et al. 2014). Because of their intrinsic low luminosities identifying a large, complete and well-defined sample of white dwarfs still remains a challenge. Much progress has been made in recent years thanks to large area surveys, first and foremost the Sloan Digital Sky Survey (SDSS; York et al. 2000; Harris et al. 2003; Eisenstein et al. 2006; Kleinman et al. 2013). The largest published catalogue of white dwarfs to date (Kleinman et al. 2013) fully exploited the spectroscopic data available at the time of SDSS Data Release 7 (DR7) and contains over 20 000 white dwarfs (of which 7424 with g ≤ 19). However not only is DR7 now outdated, but SDSS spectroscopy is only available for less than 0.01 per cent of all SDSS photometric sources. Furthermore most of SDSS's white dwarfs are only serendipitous spectroscopic targets. The true potential of SDSS's vast multiband photometric coverage still remains to be fully mined for white dwarf research, but this requires a reliable method able to select white dwarfs candidates without recourse to spectroscopy. Proper motion has been traditionally used to distinguish white dwarfs from other stellar populations. In particular many studies that contributed to the census of white dwarfs in the solar neighbourhood specifically targeted high proper motion objects (Holberg et al. 2002; Sayres et al. 2012; Limoges, Lépine & Bergeron 2013). In this paper we present a novel method which makes use of photometric data and proper motions to calculate a probability of being a white dwarf (PWD) for any photometric source within a broad region in colour space. Unlike any previous similar work, our method does not use a specific cut in colour or proper motion to generate a list of white dwarf candidates; instead it provides a catalogue of sources with an associated PWD. These PWD can then be used to create samples of white dwarf candidates best suited for different specific uses. By applying our method to the full photometric footprint of SDSS Data Release 10 (DR10), we created a catalogue which includes ∼23 000 bright (g ≤ 19) high-fidelity white dwarfs candidates (Table 1). Using this catalogue, we assess the spectroscopic completeness of the SDSS white dwarf sample.

2015ApJ...813..103M__Koss_et_al._2012_Instance_1

The stochastic accretion of gas and galaxy merger-driven gas inflows are both known triggers of supermassive black hole (SMBH) growth and nuclear activity, but the relative contributions of each is still unclear. Simulations of galaxy mergers show that they drive gas to the centers of merger-remnant galaxies (e.g., Springel et al. 2005; Hopkins & Hernquist 2009), predicting that merger-driven SMBH mass growth occurs when the black hole nears the center of the merger remnant. Observations have shown that the AGN fraction does increase with decreasing distance between two merging galaxies (Ellison et al. 2011; Koss et al. 2012; Ellison et al. 2013), but this has not been well tested at the very centers of merger-remnant galaxies because of the observational difficulty of detecting and resolving two AGNs with separations 10 kpc. This is known as the “dual AGN” phase.4 4 The separation scale expected for dual AGNs is between 0.1 and 10 kpc. The SMBHs in a merger stay at these separations for a few hundred megayears before evolving into a gravitationally bound, parsec-scale separation binary AGN system (Begelman et al. 1980). Hundreds of AGN pairs with >10 kpc separations have been discovered (Myers et al. 2008; Hennawi et al. 2010; Liu et al. 2011). However, there are only a few confirmed kiloparsec-scale dual AGNs (Junkkarinen et al. 2001; Komossa et al. 2003; Hudson et al. 2006; Rodriguez et al. 2006; Bianchi et al. 2008; Fu et al. 2011b; Koss et al. 2011, 2012; Mazzarella et al. 2012; Liu et al. 2013; Comerford et al. 2015). Dual AGNs are an intermediate evolutionary stage between first encounter and final coalescence of two merging gas-rich galaxies (e.g., Comerford et al. 2009; Liu et al. 2012), in which strong tidal interactions are more likely to influence the nuclear accretion and star formation in both galaxies (Barnes & Hernquist 1996). Indeed, galaxy merger simulations and observations clearly show that the dual AGN phase is the critical stage when SMBH growth and star formation activity are the most vigorous (e.g., Koss et al. 2012; Van Wassenhove et al. 2012; Blecha et al. 2013).

2019AandA...625A.147A__Chantzos_et_al._2018_Instance_1

Hydrocarbons also show a variety of deuterated molecules in L483. We detected C4D, with an isotopic ratio of 1.9 ± 0.6%, which is slightly above that found in TMC-1 (0.43; Turner 1989) and similar to that derived in L1527 (1.8; Sakai et al. 2009b). We also detected the singly and doubly deuterated forms of c-C3H2, with isotopic ratios of 5.1 ± 1.5% and 0.97 ± 0.29%, respectively, which are similar to the values found in L1544 (Spezzano et al. 2013) and L1527 (Sakai et al. 2009b; Yoshida et al. 2019). Moreover, c-C3HD and c-C3D2 have been surveyed in a sample of low-mass prestellar and protostellar cores (Chantzos et al. 2018), finding that the corresponding isotopic ratios are relatively uniform, within a factor of a few, and similar to those in L1544 and L483. Thanks to the high sensitivity of our line survey, we detected the different deuterated forms of the two 13C substituted isotopologs of c-C3H2, that is, c-H13CCCD, c-HCC13CD, and c-HC13CCD; the latter only tentatively. This is to our knowledge the first time these species have been detected in space. The deuterium ratios derived for c-H13CCCD and c-HCC13CD are in line with that found for c-C3HD. We also detected the deuterated form of the linear isomer of C3H2, l-C3HD, which was recently observed for the first time toward TMC-1 and L1544 (Spezzano et al. 2016). The isotopic ratio we find in L483, 3.8 ± 1.1%, is similar to the values derived in TMC-1 and L1544. Moreover, it seems that the linear isomer of C3H2 shows very similar levels of deuterium fractionation to the cyclic isomer in both low-mass prestellar and protostellar cores. The two deuterated forms of methyl acetylene, CH2DCCH and CH3CCD, which have previously been observed in TMC-1 with isotopic ratios of a few percent (Gerin et al. 1992a; Markwick et al. 2005), are also detected in L483 with slightly higher deuterium ratios (~6%). The deuterium ratio found for CH2DCCH in L483, 6.5 ± 1.9%, is similar to that derived in L1527 (4.7%; Yoshida et al. 2019).

2021AandA...656A..94G__Gronow_et_al._2021_Instance_2

Major differences between our approach and a full re-calculation of the hydrodynamics were not expected since the changes in the 14N and 22Ne abundances at the different metallicities do not alter the energy release in the hydrodynamic simulations significantly. The situation is different for deflagrations where the buoyancy, and therefore the Rayleigh-Taylor instabilities, depend on Ye. In contrast to detonations, the propagation of a deflagration front is thus affected by the metallicity (e.g., Meakin et al. 2009). The assumption we made here is confirmed by the comparison of the models presented in Table 1. Model M2a is taken from Gronow et al. (2020). The model was calculated at zero metallicity and has a total mass of 1.05 M⊙ with a He shell of 0.07 M⊙ at He ignition. Model M10_05_1, on the other hand, has a similar mass configuration, though it was calculated at solar metallicity (Model M10_05 in Gronow et al. 2021). Model M2a_pp is the same model as Model M2a, but the postprocessing step was calculated with solar metallicity instead of zero metallicity. An inspection of the abundances of Models M2a_pp and M10_05_1 at t = 100 s after He detonation ignition shows that the results of the postprocessing step with varying metallicities are in reasonably good agreement with a full re-calculation of the hydrodynamic model. The maximum difference in the yields produced in the core detonation is only 10%, while the maximum difference is 50% in the He detonation (excluding 12C in both). However, differences in the yields produced in the He detonation can in part be attributed to the slightly different setups of Model M2a (and therefore Model M2a_pp) and Model M10_05_1 at the beginning of the relaxation simulation, with the differences in the total and shell masses being less than 1% (see Gronow et al. 2021 for an explanation of the difference). Generally, the contribution of the yields from the He detonation to the total nucleosynthetic yields are small compared to those of the core detonation. Our approach is thus sufficient to derive nucleosynthetic yields for GCE studies. It saves significant computational costs as additional 3D hydrodynamical simulations of the explosion do not need to be carried out. Nevertheless, there might be slight differences visible in the observables because they are sensitive to the products of the He shell detonation (Höflich et al. 1996; Nugent et al. 1997; Kromer et al. 2010).

2021AandA...649A..84H__Geers_et_al._2007_Instance_1

The objective of this article is to study the spatial distribution and possible changes in the properties of carbon nano-dust in protoplanetary disks (PPDs). Carbon nanodust, detected under more or less organised structures and different ionisation states, constitutes a major component of dust in the interstellar and circumstellar environments. Vibrational emission bands in the near- to mid-IR from nanocarbon dust have been observed towards PPDs around most of the Herbig Ae stars, about half of the Herbig Be stars, and a few T-Tauri stars (e.g. Brooke et al. 1993; Acke & van den Ancker 2004; Acke et al. 2010; Seok & Li 2017). In contrast to large grains, these tiny and numerous carbon grains are well coupled to the gas and do not settle towards disk midplanes. This results in different spatial distributions in which tiny grains are present at the disk surfaces (e.g. Meeus et al. 2001; Habart et al. 2004; Lagage et al. 2006) and in the cavity or gaps from which the pebbles are missing (e.g. Geers et al. 2007; Kraus et al. 2013; Klarmann et al. 2017; Kluska et al. 2018; Maaskant et al. 2013). The very small carbon grains in the irradiated disk layers may have strong consequences (e.g. Gorti & Hollenbach 2008). As in the irradiated regions of the interstellar medium, they are the prevalent contributors to the energetic balance because they are very efficient at absorbing UV photons and heating the gas through the photoelectric effect. The highest fluxes of lines tracing the warm gas (e.g. [OI] 63 and [OI] 145 μm, H2 0-0 S(1), and high-J CO) are found in PPDs that show a large amount of flaring and high aromatic band strength (e.g. Meeus et al. 2013). Moreover, due to their large effective surface area, they may dominate the catalytic formation of key molecules as H2 and the charge balance. The disk structure may further depend on the level of nanograins that are coupled with the gas. Characterising the size and properties of these tiny grains through the disks, from internal to external regions, is thus of prime importance to understand the structure and evolution of PPDs.

2021MNRAS.504....1C__Ripepi_et_al._2014_Instance_1

Our first attempt to derive the $PL_{K_\mathrm{s}}$ relation has revealed a number of RRLs brighter than the main relation. We investigated their spatial distribution and found that they are mainly located in the central part of the LMC. This is shown in Fig. 5 which presents in the bottom panel the spatial distribution of the whole sample of RRLs considered in this paper (${\sim}29\, 000$), in the top panel the distribution of the RRLs which appear to be overluminous in the $PL_{K_\mathrm{s}}$ relation, and, in the middle panel, the distribution of the RRLs which were actually used to fit our final $PL_{K_\mathrm{s}}$ relation. Further investigations, performed using the Fourier parameters (ϕ31, ϕ21, R21, and R31) of the light curves available in the OGLE IV catalogue did not show any particular properties of the overluminous RRLs. On the other hand, in the period–amplitude diagram based on the I amplitudes available in the OGLE IV catalogue, the bright RRLs all show small, in some cases near-zero amplitudes, compared with regular RRLs of the same period. The decrease in amplitude at a given period can be owing to these RRLs being blended with non-variable stars. We expect the centroid of a blended source to be determined with poor accuracy (see e.g. Ripepi et al. 2014, 2015). For this reason as a further test we plotted the distribution of distances in arcsec of the VMC sources cross-matched with the OGLE IV RRLs. A clear separation is now seen in the two samples, for 94 per cent of the RRLs lying on the PL/PW relations the cross-match radius is less than 0.2 arcsec, whereas for 68 per cent of the overluminous RRLs the cross-match radius is larger than 0.2 arcsec. The average accuracy of the VMC astrometry is of the order of 0.080 arcsec both in RA and in Dec. (Cioni et al. 2011). We therefore discarded the RRLs with a cross-match radius larger than 0.2 arcsec. A total of 3252 objects were discarded. This procedure allowed us to significantly reduce the scatter on the PL/PW relations. We visually inspected the VMC images of some of the discarded RRLs, confirming that they all are clearly blended with stars and/or background galaxies. Similar investigations were performed in the past and the same effects were noted, e.g. by Ripepi et al. (2015) for Type II Cepheids. The final sample of LMC RRLs after this cleaning procedure contains 25 795 objects. This is the sample that was used as a starting point to investigate the PL and PW relations presented in the following sections. Additional RRLs were later discarded from the final fit based on a 3σ clipping procedure leading to $PL_{K_\mathrm{s}}$ relations using a clean sample of ${\sim}22\, 000$ RRLs.

2021MNRAS.506.1715D__Jones_et_al._2003_Instance_1

In direct relation to the interplay between the structure of the dark matter distribution and the baryon physics, galaxies are found in a wide range of structural hierarchies, from low-density regions to groups and clusters (see e.g. Tully 1987; Berlind et al. 2006; Yang et al. 2007), and during their lifetime they experience merging events (e.g. Mamon 1988; Tempel et al. 2017). In some cases, the mergers eventually devoid their entire neighbourhood, leaving behind a single elliptical galaxy of group-scale mass, called a fossil group galaxy (Ponman et al. 1994; Jones et al. 2003). Numerical simulations by Barnes (1989) first motivated such a hierarchical merging scenario (see also Díaz-Giménez, Muriel & Mendes de Oliveira 2008). Since then, there have been several supporting reports of X-ray sources identified as fossil groups (Santos, Mendes de Oliveira & Sodré 2007; La Barbera et al. 2009). Because most fossil systems found to date lie within z 0.2, fossil galaxy groups most likely are old, undisturbed systems due to the lack of major mergers. While some luminous galaxies experience major merger events in their evolution, fossil group galaxies acquire their mass typically through minor merger events, where the mass ratio stays below 0.3 (DOnghia et al. 2005). Simulations show that a fossil system may assemble half of its mass in dark matter by redshift z > 1, and that the assembled mass at any redshift is generally higher in a fossil than in regular groups (Dariush et al. 2007). Since this merging process is relatively fast compared to the cooling time of the surrounding gas, comparable to one to several Hubble times, fossil groups are usually found embedded in giant, X-ray luminous gas haloes (Mulchaey 2000). If a fossil system has not yet fully merged, it can be identified by another criterion, a gap in brightness of at least 2.5 mag (usually defined in the r band) between the first and fourth brightest galaxies in the group (Dariush et al. 2010; Zarattini et al. 2014) within half its virial radius.

2016AandA...596A.116S__Bochanski_et_al._2010_Instance_1

At present, however, kinematical studies of the nearby late-type stars have become somewhat foreshadowed with the advent of deep, wide-field SDSS (York et al. 2000), SDSS/SEGUE (Yanny et al. 2009) and RAVE (Steinmetz et al. 2006) surveys which have made it possible to study K–M dwarfs to much greater distances from the Sun. Using their spectroscopic catalog with radial velocities measured with an external accuracy of 7–10 km s-1 and applying photometric parallax relations, the SDSS teams have traced K–M dwarfs in the distance range up to ~2 kpc, thus providing valuable information on the spatial, velocity, and metallicity distributions with respect to vertical distance from the Galactic plane (Bochanski et al. 2007, 2011; Jurić et al. 2008; Fuchs et al. 2009; Bond et al. 2010; West et al. 2011; Schlesinger et al. 2012; Zhang et al. 2013), as well as on the luminosity and mass functions of low-mass dwarfs in the Galactic disk (Covey et al. 2008; Bochanski et al. 2010). Relatively local (| z | 500 pc) samples of late-type dwarfs from the RAVE survey, based on radial velocities accurate to ~2 km s-1 and photometrically determined distances, have been used to deduce the solar space velocity with respect to the Local Standard of Rest (Coşkunoǧlu et al. 2011; Pasetto et al. 2012a; Golubov et al. 2013). The RAVE survey has also resulted in a catalog of ~44 000 candidate active stars (Žerjal et al. 2013) which makes a major contribution to the data on chromospheric emission of cool dwarfs. In the context of these massive surveys, as well as of present-day models of the Galaxy, the samples of nearby late-type dwarfs with most accurate trigonometric parallax and radial-velocity measurements, as well as other high-quality observational data, still remain important, as they provide a fundamental framework for calibration and tests of relations between low-mass star parameters (such as, e.g., color-luminosity, mass-luminosity, activity-age, chemo-kinematic relations).

2019ApJ...874..154D__Stanway_et_al._2016_Instance_1

There are several reasons to suspect that the (galaxy-sourced) ionizing background during reionization may have been somewhat harder than the estimates given here. First, our calculations neglect the filtering effects of optically thick H i in the ISM of the host galaxy, and within the cosmic web. Absorption by this gas would have hardened the spectrum of the ionizing radiation as it escaped the galaxy and traveled through the IGM (e.g., Madau 1995; Faucher-Giguère et al. 2009; Haardt & Madau 2012). These effects were likely strongest during the last stages of reionization, when the radiation typically had to travel large distances to reach the I-fronts. Second, our calculations neglect the effects of binary star systems. Mass transfers and mergers between binary companions can extend the period over which ionizing photons are produced by the stellar population, which would harden the time-integrated spectrum (Eldridge & Stanway 2009; Stanway et al. 2016). Lastly, recent studies have suggested that the IMF in starburst galaxies may be more top-heavy than the IMF assumed here (Baugh et al. 2005; Gunawardhana et al. 2011; Marks et al. 2012; Zhang et al. 2018). Most recently, Schneider et al. (2018) measured a logarithmic slope of in the mass range 15–200 M⊙, using spectroscopic measurements of the 30 Doradus star-forming region in the Large Magellanic Cloud. (The IMF adopted here has a slope of 2.3 for M > M⊙, and a cutoff of 120 M⊙.) Each of the provided effects would work in the direction of making smaller. Based on these considerations, we argue that the lower half of Figure 2, with ≲ 1.5, is likely the most relevant region of parameter space for . In what follows, we adopt = 1.5 as our fiducial value, but we note that is only mildly sensitive to except at the fastest I-front speeds. In the next section we find that = 104 km s−1 is close to the upper limit achieved by I-fronts in cosmological simulations, which yields = 26,200 K, assuming = 1.5 (see Figure 2). This result varies by Δ = −4200 (+1600) K if we instead assume = 2.5(0.5).

2021MNRAS.505..523R__Mościbrodzka_et_al._2017_Instance_1

Recently, the EHT Collaboration completed an analysis of the linear polarization of M87*, providing novel insights into its magnetic field structure in particular (Event Horizon Telescope Collaboration 2021a,b; Goddi et al. 2021). Synchrotron emission, which dominates the millimetre image, is initially generated perpendicular to the local magnetic field, such that its polarization carries with it an imprint of the field geometry (Palumbo, Wong & Prather 2020). Then, as this polarization propagates, it is further modified by Faraday effects, important for depolarizing accretion flows down to observed levels and generating the observed rotation measure (Ballantyne, Özel & Psaltis 2007; Mościbrodzka et al. 2017; Jiménez-Rosales & Dexter 2018; Ricarte et al. 2020). Fully polarized radiative transport simulations on EHT scales have been developed in the past few decades, allowing us to link polarized images to the detailed physics of the underlying plasma and the space–time producing them (Bromley, Melia & Liu 2001; Broderick & Loeb 2006; Broderick & McKinney 2010; Porth et al. 2011; Shcherbakov, Penna & McKinney 2012; Dexter 2016; Mościbrodzka & Gammie 2018). This enabled (Event Horizon Telescope Collaboration 2021b) to discriminate between two major classes of accretion disc: a ‘Magnetically Arrested Disk’ (MAD) and ‘Standard and Normal Evolution’ (SANE). MAD accretion discs have magnetic fields strong enough to affect the disc dynamics and exhibit stronger poloidal (or non-toroidal) magnetic field components (Bisnovatyi-Kogan & Ruzmaikin 1974; Igumenshchev, Narayan & Abramowicz 2003; Narayan, Igumenshchev & Abramowicz 2003; Chael, Narayan & Johnson 2019). Meanwhile, the weaker magnetic fields of a SANE disc are sheared out by the motion of the plasma into a mostly toroidal configuration (Narayan et al. 2012; Sądowski et al. 2013; Ryan et al. 2018). The fractional linear polarization of M87*, the upper limit on its circular polarization, and most importantly the ‘twisty pattern’ of its spatially resolved linear polarization map favour ‘MAD’ models of M87* (Event Horizon Telescope Collaboration 2021b).

2019MNRAS.486.4671M__Schwenn_2006_Instance_1

CMEs are known for large-scale expulsion of magnetized plasma structures from closed magnetic field regions on the Sun. They were first detected in the coronagraphic images taken in 1971 by NASA’s OSO-7 spacecraft (Tousey 1973). However, some definite inferences for the solar wind (Eddington 1910; Birkeland 1916; Biermann 1951) as well as CMEs from the Sun (Chapman & Ferraro 1931; Eddy 1974) were made decades before their formal discovery. Following OSO-7, a series of spacecraft (Skylab, Helios, P78-1 Solwind, SOHO, Coriolis, and STEREO, etc.) have observed thousands of CMEs leading to a vast literature (Munro et al. 1979; Howard et al. 1985; Gosling 1993; Hundhausen 1999; Gopalswamy et al. 2000; Schwenn 2006; Vourlidas et al. 2010; Chen 2011; Wang et al. 2011; Webb & Howard 2012; Mishra & Srivastava 2013; Mishra et al. 2017; Harrison et al. 2018). CMEs have been observed to occur often having spatial and temporal relation with solar flares, eruptive prominences (Munro et al. 1979; Webb & Hundhausen 1987; Zhang et al. 2001; Gopalswamy et al. 2003) and with helmet streamer disruptions (Dryer 1996). Unlike CMEs from the Sun, to observe stellar CMEs are challenging because the close stellar environment cannot be spatially resolved. Although stellar CMEs have not yet been directly detected in Thomson-scattered optical light from other stars, it is believed that the extreme X-ray flares observed on stars may be in conjunction with extreme stellar CMEs (Houdebine, Foing & Rodono 1990; Wheatley 1998; Leitzinger et al. 2011; Aarnio, Matt & Stassun 2012; Osten & Wolk 2015; Vida et al. 2016). Indeed, the stellar X-ray flare, helmet streamers, and prominences observed on T Tauri Stars have shown similarities with those observed on the Sun (Haisch, Antunes & Schmitt 1995; Massi et al. 2008). The CMEs and flares themselves may not be causally related, they both seem to be involved with the reconfiguration of complex magnetic field lines within the corona caused by the same underlying physical processes, e.g. magnetic reconnection (Priest & Forbes 2002; Compagnino, Romano & Zuccarello 2017). But, even for the sun, it has been noted that not all flares are accompanied by CMEs and not all CMEs by flares (Munro et al. 1979; Harrison 1995; Yashiro et al. 2008b; Wang & Zhang 2008).

2021AandA...645A.137A__Gopal-Krishna_et_al._(2011)_Instance_1

The first INM of PG 1553+113 to our knowledge was reported by Stalin et al. (2005) – they applied the C-test to the R-band LCs (total duration of about 10 h) and found the source to be variable in one night out of two. Another two nights of INM about 13 h long were presented by Osterman et al. (2006). They found no significant INV, but they did not present statistical tests. To quantify the conclusion of Osterman et al. (2006), we used the C-test in the form C = σ/⟨e⟩, where σ is the standard deviation of the source LC and ⟨e⟩ the mean uncertainty of the source photometric measurements. We applied this test to the data listed in Table 4 of Osterman et al. (2006) and found C = 1.52 and C = 1.28 for the respective nights, which means that PG 1553+113 was non-variable. Andruchow et al. (2007, 2011) reported the results from the INM they carried out during four nights in the VR-bands (April 21 to 24, 2007) and five nights in the BR-bands (April 21 to 25, 2009), respectively. Applying the C-test, the authors found no significant variability. Gopal-Krishna et al. (2011) detected INV during three nights out of three using C- and F-tests (a total monitoring duration of 16 h in the R-band). Gaur et al. (2012) found no INV during six nights of monitoring in the BR-bands (C- and F-tests were applied). Gupta et al. (2016) presented the results from the INM during seven nights in the R-band (a total monitoring duration of about 26 h). The authors found the source to be variable in one night, non-variable in another, and probably variable in the remaining nights (F- and χ2 tests were applied). We, however, should point out that in the latter nights the variability amplitude seems to be quite close to the magnitude uncertainties. In such cases the usage of the C-test could be more appropriate (see Zibecchi et al. 2017, 2020). Pandey et al. (2019) monitored PG 1553+113 for eight nights in the VR-bands for 2–4 h each night. Employing enhanced F- and nested ANOVA tests, the authors found the source to vary on intra-night timescales for three nights. Finally, Pasierb et al. (2020) found no INV in the BVR-bands during a single night of monitoring (duration of about 3.7 h, F-test employed).

2016MNRAS.462S.376B__Bowell_et_al._1989_Instance_1

In order to assess the impact of the brightness enhancement due to the phase function effects in the light scattering of the coma dust, we apply different phase function corrections to the measured coma brightness of 67P for the 10 000 km aperture radius. In general, the phase function reduces the reflected coma magnitude value. For small phase angles α (typically up to 30°), a linear reduction with α can be applied with typical parameters β for comets between 0.02 and 0.06 (Lamy et al. 2004). Alternatively, the two-parameter phase function of Schleicher, Millis & Birch (1998), based on 1P/Halley measurements, is available or the phase angle correction by Müller (1999) that also considers geometric projection effects by a non-spherical coma, symmetric to the radial direction with respect to the Sun. Fornasier et al. (2015) obtained results for the 67P nucleus surface reflectivity applying a HG-type phase function (Bowell et al. 1989) with a best-fitting G of −0.13. Fig. 5 shows the phase angle-corrected afρ values of 67P for the various phase function solutions. In Fig. 5, β = 0.04 is used as mean value for the linear phase function that is lower than the β value found for the nucleus of 67P (0.059–0.076; Tubiana et al. 2011; Lowry et al. 2012). A second HG-type phase function is applied as well with G = 0.15 that is found from many asteroid light curves (Tedesco 1989). Obviously, the afρ value for zero phase angle depends on the phase darkening and is highest for the Bowell-type phase function with G = −0.13 (as obtained for the 67P nucleus by Fornasier et al. 2015). Two phase functions provide a close to linear decrease of afρ with solar distance over the time interval from the end of 2015 September to the beginning of 2016 April, i.e. the linear phase function with β = 0.04 and the Bowell-HG-type phase function with G = 0.15. The phase functions of Schleicher et al. (1998) and of Müller (1999) can approximate the afρ data well between the end of September and end of December, but do not describe well the 67P dust activity measured in 2016.

2015ApJ...805..134M__Yoo_et_al._2014_Instance_1

The physics of asymmetric inflow reconnection has been investigated in detail for fully ionized plasmas. One of the principal applications of this work has been Earth's dayside magnetopause. Asymmetric inflow reconnection has also been investigated in the context of Earth's magnetotail and elsewhere in the magnetosphere (Oieroset et al. 2004; Muzamil et al. 2014), the solar atmosphere (Murphy et al. 2012; Nakamura et al. 2012; Su & van Ballegooijen 2013; Su et al. 2013), laboratory experiments (Yamada 2007; Murphy & Sovinec 2008; Yoo et al. 2014), and plasma turbulence (Servidio et al. 2009, 2010). Cassak & Shay (2007) performed a scaling analysis for asymmetric inflow reconnection. They found that the outflow is governed by a hybrid upstream Alfvén speed that is a function of the magnetic field strength and density in both upstream regions (see also Birn et al. 2010) and that the flow stagnation point in the simulation frame and magnetic field null were not colocated. The structure and dynamics of asymmetric reconnection in fully ionized collisionless and two-fluid plasmas have been studied previously in several works (e.g., Swisdak et al. 2003; Cassak & Shay 2008, 2009; Mozer et al. 2008; Murphy & Sovinec 2008; Pritchett 2008; Mozer & Pritchett 2009; Pritchett & Mozer 2009; Malakit et al. 2010, 2013; Aunai et al. 2013a, 2013b). Simulations of the plasmoid instability during reconnection with asymmetric upstream magnetic fields by Murphy et al. (2013) showed that the resultant magnetic islands developed primarily into the weak-field upstream region. Because the reconnection jets impacted the islands obliquely rather than directly, the islands developed net vorticity. In addition to asymmetric inflow reconnection, several groups have investigated asymmetric outflow reconnection (e.g., Oka et al. 2008; Murphy 2010; Murphy et al. 2010) and reconnection with three-dimensional asymmetry (e.g., Al-Hachami & Pontin 2010; Wyper & Jain 2013).

2021ApJ...910...18C__Rubele_et_al._2018_Instance_1

The LMC is known to have brought a large population of star clusters as it has been accreted onto the Milky Way (Bica et al. 2008); thus, it is possible that DELVE 2 may share a similar history to the thousands of known star clusters in the Magellanic system. The LMC star cluster formation history is believed to be three-staged, including a period of rapid cluster star formation in the early universe (τ ≳ 10 Gyr), followed by a long quiescent period between ∼10 and ∼2–4 Gyr ago and then by a period of rapid star cluster formation extending to the present day, potentially due to the interaction between the LMC and SMC (Harris & Zaritsky 2009; Weisz et al. 2013; Rubele et al. 2018; Ruiz-Lara et al. 2020). One consequence of this period of quiescence in the LMC cluster formation history is the so-called “age gap” in the age distribution of LMC clusters, with a small (N ≲ 20) population of globular clusters with ages comparable to most known Milky Way globular clusters, separated by the gap from a much larger population of less massive young clusters (e.g., Bertelli et al. 1992; Girardi et al. 1995; Olszewski et al. 1996). These two populations of clusters obey an overarching age–metallicity relation, within which the older clusters (τ > 12 Gyr) are significantly more metal-poor (−2.2 ≲ [Fe/H] ≲ −1.2) compared to the younger population of clusters ([Fe/H] ≳ −0.7; Meschin et al. 2014).38 38 We note that Gatto et al. (2020) recently discovered 16 cluster candidates believed to be within the LMC cluster age gap (4 Gyr ≲ τ ≲ 10 Gyr). Therefore, although the photometrically derived metallicity and age for DELVE 2 are limited in accuracy by the small number of red giant branch stars available to precisely constrain these properties through synthetic isochrone fitting, it is clear that DELVE 2 is more consistent with an old, metal-poor stellar population and thus the former class of LMC clusters (provided the system is not a dwarf galaxy, as discussed in the previous subsection).

2017ApJ...834L..21A__Larsen_&_Lane_1994_Instance_1

The experiment was carried out using the VULCAN laser facility at the Rutherford Appleton Laboratory (Danson et al. 1998), using the setup shown in Figure 1(a). A laser pulse of duration ∼1 ns and energy ∼ 70 J, was focused onto a 50 μm thick gold foil, at an incidence angle of ∼ 45 ° to a peak intensity of ∼1015 W cm−2. The interaction of the nanosecond pulse with the gold foil leads to the generation of ablated plasma that is mainly constituted of thermally distributed gold ions and faster lighter ions with an average energy per nucleon of tens of keV (Tan et al. 1984; Gitomer et al. 1986). The lighter ions, such as protons and carbon ions, originate from contaminant layers (water vapor and hydrocarbon) typically present on the surface of the targets (Gitomer et al. 1986). The laser–target interaction was enclosed in a gas cell filled with pure nitrogen at a controlled pressure of ∼10−1 mbar. Hydrodynamic simulations using the code HYADES (Larsen & Lane 1994) indicate that the gas becomes fully ionized within 100 ps from the start of the laser irradiation by the X-rays emitted from the target (Dean et al. 1971), resulting in a stationary plasma with an electron density and temperature of ∼3 × 1016 cm−3 and T e ∼ 1 keV, respectively. These values imply an electron Debye length of λ D ∼ 1.4 μ m and an ion-acoustic speed of C s ∼ 2.2 × 10 5 m s − 1 . Moreover, the Coulomb logarithm for electron–electron and ion–ion collisions are of the order of 6 and 11, respectively, indicating a characteristic timescale for collisions of τ ee ∼ 36 ns and τ ii ∼ 600 ns , respectively. The PPI technique (Borghesi et al. 2002; Sarri et al. 2010) was employed to investigate the interaction of the ablated plasma with the background plasma. The probe proton beam was generated by focusing a second laser pulse of ∼1 ps duration and ∼50 J energy, to an intensity of ∼ 10 19 W cm−2 onto a thin gold foil (thickness ∼20 μm). In the experimental arrangement shown in the Figure 1(a), the distance between proton source and interaction region was l ≃ 4 mm , and the detector was placed at L ≃ 38 mm from the interaction region, giving an intrinsic geometrical magnification of M ≈ ( l + L ) / l ∼ 10.5 (Borghesi et al. 2002). A stack of several layers of dosimetrically calibrated (Kirby et al. 2011) RadioChromic Films (RCF), was used to detect the proton beam. The two lasers were temporally delayed so that a proton with an energy of 13 MeV traverses the interaction region at the time t 0 = 180 ± 20 ps after the start of the long-pulse irradiation, where the start of the interaction corresponds to 1/10 of the peak intensity. The error in defining the beginning of the interaction is mainly due to the systematic error in the synchronization of both laser beams. This does not affect the temporal resolution of the PPI technique, which is on the order of picoseconds (Sarri et al. 2010).

2015AandA...580A..71L__Sutton_et_al._(2013)_Instance_1

The simplest two component model (power law + disk) is a phenomenological model often used to describe the spectra of ULXs as an empirical description of a disk plus corona geometry. In the presence of a cool (kT ~ 0.1−0.4 keV) and luminous (L ~ 1039−1040 erg/s) disk, it allows inferring the presence of intermediate-mass black holes (e.g., Makishima et al. 2000). This is not the case for M33 X-8, where the disk component describes the high-energy part of the spectrum well and appears to be hot (kT ~ 1.15 keV), leaving a soft excess that is accounted for by the power law. The overall disk parameters are then inconsistent with a massive black hole, but instead are more typical of an ordinary stellar mass black hole: using the relationship between mass, temperature, and luminosity in a standard disk (see, e.g., Makishima et al. 2000), we derive a mass of ~10 M⊙ for a nonrotating black hole, consistent with the estimation obtained by data from other satellites (e.g., Foschini et al. 2006; Weng et al. 2009; Isobe et al. 2012). Sutton et al. (2013) developed a classification scheme based on a disk+power law fit, to be applied to ULX spectra, according to which the spectral state of an ULX source can be defined by the disk temperature, the power-law slope, and the ratio between the flux contribution of the two spectral components in the 0.3−1 keV band. Our result is consistent with that found by Sutton et al. (2013) using XMM-Newton data, and, according to their classification, it identifies M33 X-8 as a broadened disk source, or in other words, as a source whose spectrum is dominated by emission from a hot disk (see Table 2) and where the additional soft component may be the effect of an unrealistic description of the disk spectrum by the diskbb model. In fact, such hot-disk/soft power-law spectra are difficult to explain in the context of the analogy of ULXs with GBHs: the thermal state of GBHs is indeed characterized by a hot disk, but the presence of a soft power-law-like component in addition to the disk is unusual, and its physical interpretation is not simple: if this component is due to the presence of a Comptonized corona, we do not expect it to be dominant at energies lower than the temperature of the seed photons that come from the disk.

2022ApJ...924...97W__Zhang_&_Mészáros_2001_Instance_1

Using all GRBs showing X-ray plateau phases, Dainotti et al. (2008) discovered a tight correlation between L0 and tb (the Dainotti relation). Subsequently, the Dainotti relation has been used to measure cosmological parameters (Cardone et al. 2009, 2010; Dainotti et al. 2013; Postnikov et al. 2014). Cardone et al. (2010) relied on this correlation to build a Hubble diagram of 66 GRBs and present a preliminary constraint on cosmological parameters. The errors on the cosmological constraints are large, which may be related to the nature of GRB light curves and in part due to the sample selection. In previous works, GRBs with X-ray plateaus were used to derive the Dainotti relation, and then to constrain cosmological parameters. However, a newborn magnetar can be spun down through a combination of electromagnetic dipole and gravitational-wave quadrupole emission (Shapiro & Teukolsky 1983). The X-ray luminosity of GRBs is given by the energy input from the electromagnetic and gravitational waves into the surrounding medium (Dai & Lu 1998; Zhang & Mészáros 2001; Metzger et al. 2011). Therefore, in order to standardize GRBs as standard candles through the Dainotti relation, it is recommended that X-ray plateaus caused by the same physical mechanism are used (electromagnetic dipole radiation or gravitational waves). Similar to supernova cosmology, only SNe Ia from accretion channels can be treated as standard candles. Following the Dainotti correlation (Dainotti et al. 2008) between the plateau luminosity and the end time of the plateau in X-ray afterglows, we further confirm this relation out to redshift z = 5.91. In this paper, we perform a first attempt to standardize long GRBs with X-ray plateaus dominated by electromagnetic dipole radiation as reliable standard candles. Using the same method, Hu et al. (2021) considered two other kinds of GRBs to constrain cosmological parameters, i.e., short GRBs with plateau phases dominated by magnetic dipole radiations and long GRBs with gravitational-wave-dominated plateau phases. It is interesting to note that this correlation also holds for the optical sample (Dainotti et al. 2020b).

2021AandA...653A..85S__However,_Genovali_et_al._(2014)_Instance_2

Figure 6 shows the orbital eccentricities as a function of [M/H] for the metal-rich disc sample. The solid lines correspond to the required eccentricity (see Eq. (2)) for different values of ISM radial metallicity gradients: −0.10 dex kpc−1 (black), −0.07 dex kpc−1 (our measured gradient for young stars in Table 1; see also Minchev et al. 2018, red), −0.04 dex kpc−1 (orange), and −0.06 dex kpc−1 (Cepheids analysis from Genovali et al. 2014, green). For the three first cases, we assumed ISM[M/H](R⊙) = 0.0 to estimate Rbirth from the stellar metallicity. However, Genovali et al. (2014) have their own zero point, defined as: [Fe/H] = −0.06 * Rg + 0.57, with a clear shift in the relation compared to the other ones assumed in this work. The impact of the ISM gradient value and the zero-point assumption on the derived Rbirth, and therefore on the required eccentricity to reach the solar vicinity without the need for churning, is clearly observed. As described in Hayden et al. (2020), given the measured [M/H] and eccentricity, stars lying to the left are able to reach the solar neighbourhood through blurring, while the stars to the right of the line are possible candidates to have migrated through churning. This is the case for most of the SMR stars (70% of the SMR stars lie below the line that corresponds to the Cepheids analysis); they are therefore likely to have been brought to the solar neighbourhood by churning, which is in close agreement with previous studies (e.g., Kordopatis et al. 2015a; Wojno et al. 2016). However, it is worth noting that the observed metallicity distribution function in Fig. 2 peaks around 0.2 dex, which is higher than previous reported solar vicinity MDFs (see e.g., Fuhrmann et al. 2017). A possible ignored bias towards more metal-rich objects in the sample selection could be pulling the percentage of possible migrators to higher values. Among the entire distribution, our churned candidates with [M/H] >  + 0.1 comprise around 17% of the sample. If we constrain the number of migrators to only stars with [M/H] >  + 0.25, the global percentage decreases to 8% of the sample.

2015ApJ...799...55G__Klassen_et_al._2000_Instance_2

While the angular extent of IP shocks can be directly investigated using multi-point in situ measurements, the size of coronal shocks can only be indirectly inferred via remote-sensing observations of the electromagnetic emissions associated with them. According to Nelson & Robinson (1975), the average angle subtended at the solar surface by fundamental metric type II radio emission sources is 43°. Aurass et al. (1994) found particular cases with larger, double type II source structures covering a separation angle beyond 90°. Type II radio sources often show non-radial propagation trajectories (see Mann et al. 2003, and references therein). Wave-like large-scale disturbances propagating over the solar disk in extreme ultraviolet observations (usually referred to as “EIT waves” or “EUV waves”) are in close empirical correlation with type II radio bursts (Klassen et al. 2000). Most EIT waves are accompanied by CMEs, and observations and MHD modeling suggest that they are driven by the lateral expansion of CMEs, while the ultimate nature of the phenomenon remains under debate and could consist of true waves, pseudo waves (e.g., reconnection fronts), or a combination of both (Patsourakos & Vourlidas 2012; Nitta et al. 2013b, and references therein). According to Patsourakos & Vourlidas (2012), EIT waves can reach distances up to 1.3 R (850 Mm) from the source. Single-case studies reported some EIT waves covering a whole solar hemisphere (Klassen et al. 2000; Kienreich et al. 2009; Thompson & Myers 2009). Connections between EIT waves and SEP events have been often suggested (e.g., Bothmer et al. 1997; Krucker et al. 1999), and recently Rouillard et al. (2012) hypothesized that the EIT wave can be used to track the expansion of a coronal shock responsible for particle acceleration. Other authors question the EIT wave acceleration scenario for SEPs, with many EIT waves being observed at well-connected positions having no associated SEP increase (Miteva et al. 2014).

2021AandA...653A..36M__Goulding_&_Alexander_(2009)_Instance_2

The SFG sample was constructed using the Great Observatories All-Sky LIRG Survey (GOALS sample, Armus et al. 2009), from which we extracted 158 galaxies, with data from Inami et al. (2013), who report the fine-structure lines at high resolution in the 10 − 36 μm interval, and Stierwalt et al. (2014), who include the detections of the H2 molecular lines and the PAH features at low spectral resolution. For those galaxies in the GOALS sample that have a single IRAS counterpart, but more than one source detected in the emission lines, we have added together the line or feature fluxes of all components, to consistently associate the correct line or feature emission to the total IR luminosity computed from the IRAS fluxes. To also cover lower luminosity galaxies, as the GOALS sample only includes luminous IR galaxies (LIRGs) and ultra-luminous IR galaxies (ULIRGs), we included 38 galaxies from Bernard-Salas et al. (2009) and Goulding & Alexander (2009), to reach the total sample of 196 galaxies with IR line fluxes in the 5.5 − 35 μm interval in which an AGN component is not detected. For the Bernard-Salas et al. (2009), Goulding & Alexander (2009), and the GOALS samples, we excluded all the composite starburst-AGN objects identified as those with a detection of [NeV] either at 14.3 or 24.3 μm. It is worth noting that the original samples from Goulding & Alexander (2009) and Bernard-Salas et al. (2009) have spectra solely covering the central region of the galaxies. To estimate the global SFR, we corrected the published line fluxes of the Spitzer spectra by multiplying them by the ratio of the continuum reported in the IRAS point source catalogue to the continuum measured on the Spitzer spectra extracted from the CASSIS database (Lebouteiller et al. 2015). We assumed here that the line emission scales (at first order) with the IR brightness distribution. In particular, we considered the continuum at 12 μm for the [NeII]12.8 μm and [NeIII]15.6 μm lines, and the continuum at 25 μm for the [OIV]25.9 μm, [FeII]26 μm, [SIII]33.5 μm, and [SiII]34.8 μm lines. This correction was not needed for the AGN sample and the GOALS sample because of the greater average redshift of the galaxies in the 12MGS and GOALS samples. In particular, the 12MGS active galaxy sample has a mean redshift of 0.028 (Rush et al. 1993), while the GOALS sample has a mean redshift of 0.026. The galaxies presented by Bernard-Salas et al. (2009) have instead an average redshift of 0.0074, while the sample by Goulding & Alexander (2009) has an average redshift of 0.0044. For the other lines in the 10 − 36 μm interval, Goulding & Alexander (2009) did not report a detection, and we used the data presented in Bernard-Salas et al. (2009) for a total of 15 objects. Both Bernard-Salas et al. (2009) and Goulding & Alexander (2009) reported data from the high-resolution Spitzer-IRS spectra. Data in the 50 − 205 μm interval were taken from Díaz-Santos et al. (2017). For the GOALS sample, 20 starburst galaxies were taken from Fernández-Ontiveros et al. (2016), and 23 objects were taken from the ISO-LWS observations of Negishi et al. (2001). As a result, we obtained a total sample of 193 objects. Lastly, the PAH features’ fluxes were measured from the low-resolution Spitzer-IRS spectra by Brandl et al. (2006), including 12 objects from the sample of Bernard-Salas et al. (2009) and 179 objects from Stierwalt et al. (2014).

2018AandA...613A..50C__Reiners_et_al._2013_Instance_1

As previously mentioned, the small amplitude of the RV modulation in the NIR as measured by GIANO contemporaneously with REM photometry is intriguing. Assuming that the depth of the spectral lines relative to their adjacent continuum is constant and considering a spot with a contrast Cs at latitude ϕ with a filling factor fs, we expect an RV modulation approximately of Csfsvsinicosϕ (cf. Saar et al. 1997; Desort et al. 2007) that is ≈ 0.75−1.0 km s−1 for a spot at ϕ = 60°, which is remarkably higher than what has been observed. A cool spot at a higher latitude would reduce the amplitude of the NIR wide-band flux modulations; a quenching of the convective shifts or the Zeeman effect also do not appear to be viable explanations because they increase the effect of a cool spot on the RV at NIR wavelengths (Reiners et al. 2013). Nevertheless, the variation of the relative depths of the spectral lines in the NIR cannot be neglected and it is the dominant effect in the cool spot responsible for the large photometric modulation in the infrared. The relative line depths are strong functions of the continuum opacity and of the degree of element ionization both remarkably varying in a cool spot area with respect to the unperturbed photosphere. In general, these effects produce a remarkable increase of the relative depths of the spectral lines in the cool spot. This compensates for the decrease of the continuum intensity in the spot, reducing the distortions of the spectral line profiles and yielding an RV variation in the NIR smaller than expected from the wide-band photometric variation in the case of constant relative line depths. In any case, a quantitative analysis is not warranted by our data, since a larger number of observations would be required. This scenario suggests a need to better investigate this kind of target, since they might go through specific activity phases during which the VIS and NIR RV amplitudes are similar, possibly resulting in false positives. Looking at the curve phase shifts might give crucial information in these cases (see e.g. the recent result by Hatzes et al. 2018 for the K-giant γ Draconis).

2021MNRAS.506.5015H__Page_et_al._2004_Instance_1

Due to their high core temperatures at birth, neutron stars cool pre-dominantly by neutrino emission at ages ≲ 106 yr (Potekhin, Pons & Page 2015). The most rapid changes of surface temperature occur early, first when the temperature of the outer layers achieves equilibrium with the rapidly cooling core at an age of ≲ 100 yr (Lattimer et al. 1994; Gnedin, Yakovlev & Potekhin 2001; see also Nomoto & Tsuruta 1987) and then when the temperature drops below the critical temperature for core neutrons to become superfluid, which activates the efficient neutrino emission process of Cooper pair formation and breaking (Gusakov et al. 2004; Page et al. 2004). The rapid cooling of the CCO in Cassiopeia A (at a ten-year rate of ≈2.2 ± 0.2 or 2.8 ± 0.3 per cent, depending on whether NH varies between observations) indicates the latter starts to take place at an age of ∼200 yr (Page et al. 2011; Shternin et al. 2011). Neutron star cooling models predict that by an age of several hundred years, the cooling rate will be 1 per cent per decade. From the 1σ temperature uncertainties of our fit results with model parameters linked between observations (see Tables 4 and 7), we estimate upper limits on the ten-year cooling rates of 6 per cent for XMMU J172054.5−372652 and 17 per cent for CXOU J160103.1−513353. We measure a possible increase in temperature of ∼4 ± 2 per cent (accompanied by a decrease in emission area; see Table 5 and 6) for 1WGA J1713.4−3949. We also perform fits of the spectra of XMMU J172054.5−372652 and CXOU J160103.1−513353 which allow the temperature to be different between each observation, and the results are shown in Table 9 and Fig. 8 (analogous results for 1WGA J1713.4−3949 are shown in Fig. 3). We point out that we are concerned in Fig. 8 with relative changes in temperature and not in absolute temperature differences between CCOs since absolute temperatures depend on a variety of factors that are intrinsic to each CCO and may be different among CCOs in our sample, e.g. mass (and hence neutrino cooling rate) and radius (and hence gravitational redshift) and envelope composition and thickness. Nevertheless it is noteworthy that the temperatures of all three older CCOs appear to be higher than those of Cassiopeia A. Unlike for the 340 yr old CCO in Cassiopeia A, we do not see that temperatures of the 600 yr old XMMU J172054.5−372652 and 1000 yr old CXOU J160103.1−513353 are changing, at least within measurement uncertainties.

2020AandA...637A..59A__Ziurys_et_al._(2018)_Instance_1

Several molecules show a large discrepancy between the abundances derived from observations and calculated by chemical equilibrium, although it is not as severe as for the molecules discussed above. We refer to PN in O-rich stars and H2S in C-rich stars, which are indicated by hatched rectangles in Fig. 2. For PN in O-rich AGB atmospheres, the disagreement between the observed abundances, (1–2) × 10−8 (Ziurys et al. 2018), and the calculated maximum chemical equilibrium abundance is almost three orders of magnitude. However, uncertainties on the observational and theoretical sides mean that the true level of disagreement is unclear. For example, while Ziurys et al. (2018) derived a PN abundance of 10−8 relative to H2 in IK Tau, De Beck et al. (2013) and Velilla Prieto et al. (2017) derived higher abundances, (3–7) × 10−7, in this source. When we give preference to these latter abundances, the level of disagreement would be even higher. On the other hand, the formation enthalpy of PN is rather uncertain (see Lodders 1999), which directly translates into the calculated chemical equilibrium abundance. In this study we adopted the thermochemical data for PN from Lodders (1999), who gives preference to a formation enthalpy at 298.15 K of 171.5 kJ mol−1, while other compilations such as JANAF use lower values that would result in higher chemical equilibrium abundances for PN. This would reduce the level of disagreement. In the case of H2S in C-rich AGB stars, the calculated maximum chemical equilibrium abundance is 7 × 10−11, while the value derived from observations is ~50 times higher. In this case, the observed abundance is based on the detection of only one line in only one source (see Agúndez et al. 2012), and thus it has to be viewed with some caution. In summary, the main failures of chemical equilibrium to account for the observed abundances of parent molecules in circumstellar envelopes are NH3, HCN, CS, SO2, and possibly PN in M-type stars, H2O and NH3 in S-type stars, and the hydrides H2O, NH3, SiH4, PH3, and perhaps H2S as well in C-type stars. The large discrepancies between the abundances derived from observations and those calculated with chemical equilibrium necessarily imply that nonequilibrium chemical processes must be at work in AGB atmospheres. Any invoked nonequilibrium scenario must account for all these anomalously overabundant molecules, but must also reproduce the remaining molecular abundances that are reasonably well explained by chemical equilibrium. No scenario currently provides a fully satisfactory agreement with observations, although two mechanisms that can drive the chemical composition out of equilibrium have been proposed.

2018ApJ...864...90M__Shields_1992_Instance_1

The LLAGN interpretation of LINERs was initially motivated by their significant X-ray emission (Ferland & Netzer 1983; Halpern & Steiner 1983). Although LLAGNs are found via radio and X-ray observations in the majority of LINERs (Dudik et al. 2005, 2009; Nagar et al. 2005; Filho et al. 2006; Flohic et al. 2006; González-Martín et al. 2009), they are not powerful enough to photoionize the gas in their vicinity on ∼100 pc (i.e., a few arcseconds in nearby galaxies) scales on which the characteristic emission lines are detected (Flohic et al. 2006; Eracleous et al. 2010a, and references therein). Imaging studies of LINERs have found extended line-emitting regions and complex circumnuclear dust morphologies that might obscure and further prevent the LLAGNs from fully ionizing the surrounding gas (Barth et al. 1999; Pogge et al. 2000; Simões Lopes et al. 2007; González Delgado et al. 2008; González-Martín et al. 2009; Masegosa et al. 2011). Wolf-Rayet stars (i.e.,“warmers”; Terlevich & Melnick 1985) could successfully mimic the X-ray emission produced by a LLAGN, as well as provide the hard ionizing photons necessary to explain the relative intensities of the observed optical emission lines. If Wolf-Rayet stars were the primary source of ionizing photons for LINER-like emission lines, then most LINERs would be in the immediate post-starburst phase, which is unlikely given their high occurrence rate (Ho et al. 1997b) and stellar populations (Cid Fernandes et al. 2004; González Delgado et al. 2004). Compact starbursts containing hot O stars offer an alternative explanation for the relative intensities of LINER emission lines (Filippenko & Terlevich 1992; Shields 1992), but have difficulty explaining the broad Balmer emission wings often seen in LINER spectra (Filippenko 1996), since this would require long-lived supernova remnants. Moreover, the census of stellar populations in LINERs by González Delgado et al. (2004) and Cid Fernandes et al. (2004) does not show a high incidence of compact, nuclear starbursts. pAGB stars and planetary nebulae (Binette et al. 1994; Taniguchi et al. 2000; Yan & Blanton 2012; Belfiore et al. 2016) are more plausible stellar-based models, but are applicable only to a subset of LINERs due to their inability to explain large Hα equivalent widths (Ho et al. 2003). Shock models can adequately describe the off-nuclear optical and ultraviolet (UV) emission-line spectra of some LINERs, such as M87, obtained with the Hubble Space Telescope (HST; see Dopita et al. 1996, 1997; Sabra et al. 2003). Meanwhile the UV spectra of some LINER nuclei, such as NGC 4579, do not have emission line ratios that are well described by shock excitation, and yet show high ionization lines that would require a hard extreme UV source, such as the continuum from an active galactic nucleus (AGN), or fast shocks (Barth et al. 1996; Dopita et al. 2015). Successful shock models could have a wide range of shock velocities (Filippenko 1996), but the gas must be continuously shocked to maintain LINER-like ratios. The shocks could potentially be driven by radio jets, which are fairly common in LINERs and whose kinetic power is considerably higher than the electromagnetic luminosity of the LLAGNs (Filho et al. 2002; Nagar et al. 2005; Maoz 2007). Alternatively, the shocks could result from supernovae or winds from either young or evolved stars.

2016ApJ...830...15J__Rice_et_al._2011_Instance_1

T Tauri stars in general are known to flare (e.g., Gahm 1990; Guenther & Ball 1999). A potentially better analog of the type of variable Hα emission expected from chromospheric emission and flaring on PTFO 8-8695 is the WTTS V410 Tau, with v sin i = 77.7 km s−1 (e.g., Carroll et al. 2012) compared to the measured v sin i = 80.6 ± 8.1 km s−1 for PTFO 8-8695 (van Eyken et al. 2012). The Hα emission equivalent width (3 Å with a typical value ∼1–2 Å ) on V410 Tau (e.g., Hatzes 1995; Fernández et al. 2004; Mekkaden et al. 2005) is weaker than seen on PTFO 8-8695, although V410 Tau has an earlier spectral type that raises the continuum level without necessarily affecting the strength of the chromospheric emission. V410 Tau has been observed to flare in a number of studies. Outside of flares, the Hα line of V410 Tau is fairly symmetric, relatively narrow, and is similar in shape to the chromospheric Hα profiles for PTFO 8-8695 seen in Figures 3, 5, and 6 (Hatzes 1995; Fernández et al. 2004; Skelly et al. 2010). The Hα line of V410 Tau can grow much stronger and broader during a flare, and also show asymmetries; however, the observed asymmetries seen during flares do not show excess emission with apparent peaks shifted out to greater than ±200 km s−1 (Hatzes 1995; Rice et al. 2011) as seen here in PTFO 8-8695. The typical pattern in a flare is for the line to very rapidly (timescale of a few minutes) strengthen and broaden with only a slight asymmetry developing. The strength and width of the line then decay exponentially with a timescale of ∼1 hr for strong flares (e.g., Fernández et al. 2004). This is not the temporal behavior observed in PTF08-8695. There is at least one additional piece of evidence against the flaring interpretation for the excess Hα emission seen in PTFO 8-8695. Whenever V410 Tau shows flare emission in Hα, significant He i 5876 Å emission also appears. This He i line is covered in the echelle formats of both our McDonald and Kitt Peak data. We have searched both data sets for evidence of this emission, including co-adding the spectra when the Hα emission appears stationary (UT 9:44 to 11:17 for McDonald; UT 6:45 to 8:25 for Kitt Peak) to increase the signal-to noise. No evidence of He i emission is seen. Lastly, if the observed excess Hα emission seen in Figures 2 and 7 were the result of a stellar flare, it would be a remarkable coincidence that the flare-induced asymmetry just happened to appear at and move with the same velocity position in the line profile as that expected for the planetary companion. In particular, the motion shown in Figures 2 and 4 where the excess emission first appears strongly on one side of the line profile and then moves to the other side has not to our knowledge been observed in the Hα emission of flare stars. Flares have been observed on PTFO 8-8695 (van Eyken et al. 2012; Ciardi et al. 2015), and while flares on this star likely will produce changes in the strength and shape of the Hα emission line, we believe all the points described above argue strongly against a purely stellar origin.

2017AandA...605A...5S__Dokkum_2001_Instance_1

The Hα imaging for the galaxies discussed in this paper was generated from two different sources: NGC 3628 was observed on May 8, 1991 using the ESO New Technology Telescope (NTT, red arm of the ESO Multi-Mode Instrument (EMMI)) for 900 s in Hα and 300 s in R as part of ESO program 047.01-003. The detector was a Ford 20482 single CCD (ESO CCD #24). Both images were taken with subarcsecond seeing. The data and all relevant calibration files taken during the run and a few days before and after the observing run were retrieved from the ESO archive and re-reduced by us using IRAF in the usual manner. L.A. Cosmic (van Dokkum 2001) was used to clear the image of cosmic rays to produce the final Hα and final R image. The continuum subtracted image was then produced by aligning Hα and R band, homogenizing the PSF, and subtracting the appropriately scaled R image from the Hα image (e.g., Skillman et al. 1997). To determine the scaling factor, we measured the apparent fluxes for several stars in the field on both the Hα and R images. Scaling, subtraction, and correction to reach an optimal correction of the continuum light of the galaxy was performed with our own IRAF scripts. The NGC 4522 Hα data were taken directly from the work of Koopmann et al. (2001) and the Galaxy On Line Database Milano Network (GOLDMine, Gavazzi et al. 2003). The images were astrometrically calibrated to the Digitized Sky Survey (DSS) system. Flux calibration of the continuum subtracted Hα images was performed by transferring the total Sloan Digital Sky Survey (SDSS) r′ band flux of each galaxy to the its continuum image. With the derived scaling factor (see above), the flux scale can then be directly transferred to the continuum corrected Hα image. A conversion from magnitudes to flux was performed using the fact that SDSS is calibrated in AB magnitudes, so that the zero-point flux density of each filter is 3631 Jy (with 1 Jy = 1 Jansky = 10-26 W Hz-1 m-2 = 10-23 erg s-1 Hz-1 cm-2)1.

2021MNRAS.503.6155C__Lovisari_et_al._2017_Instance_1

Galaxy clusters are the traces of the formation of the largest structures in the Universe and so reliable tools to investigate structures formation and evolution. In principle, this is possible only if and when we have full knowledge of the properties of these objects. The total mass (i.e. the total amount of the dark matter (DM), the intracluster medium (ICM), and the stellar components) is an invaluable quantity when exploring the abundances of clusters along the redshift: a standard way to infer cosmological parameters such as the mean matter density Ωm and the amplitude of matter perturbations σ8(Planck Collaboration XIII 2016). Furthermore, under the assumption of a simple self-similar model (Kaiser 1986; Voit 2005), we could derive the total mass of the clusters from a few observables in optical, X-ray, or millimetre band (Giodini et al. 2013). This approach results in a few scaling relations valuable when we are interested to obtain averaged results based on some statistics. However, it is prone to the assumed simplified approximations: hydrostatic equilibrium and isothermal and spherical distribution for DM and ICM (Bryan & Norman 1998). It is well known that the hydrostatic equilibrium in haloes is not always satisfied, due to non-thermal pressure contributions from internal motions and turbulence (see e.g. Fang, Humphrey & Buote 2009; Lau, Kravtsov & Nagai 2009; Laganá, de Souza & Keller 2010; Rasia et al. 2012; Nelson, Lau & Nagai 2014; Yu, Nelson & Nagai 2015; Biffi et al. 2016; Eckert et al. 2019; Angelinelli et al. 2020; Ansarifard et al. 2020; Gianfagna et al. 2020; Green et al. 2020), pointing out the impact that the dynamical state of those large gravitational bounded objects should have. Several attempts have been made to infer clusters dynamical state, using both observational data and simulations, by analysing the images of the emission in optical (see e.g. Ribeiro, Lopes & Rembold 2013; Wen & Han 2013) and in the X-ray band (see e.g. Rasia, Meneghetti & Ettori 2013; Lovisari et al. 2017; Nurgaliev et al. 2017; Bartalucci et al. 2019; Cao, Barnes & Vogelsberger 2020; Yuan & Han 2020) or of the diffusion of the cosmic microwave background (CMB) photons by thermal Sunyaev–Zel’dovich (tSZ) effect in the millimetre band (Cialone et al. 2018; De Luca et al. 2020, hereafter DL20), or a combination of some of them (see e.g. Mann & Ebeling 2012; Molnar, Ueda & Umetsu 2020; Ricci et al. 2020; The CHEX-MATE Collaboration 2020; Zenteno et al. 2020). Among the possibilities, we have to mention the studies of the clusters morphology in X-ray and tSZ maps. Several indicators are commonly used, such as asymmetry parameter (Schade et al. 1995), light concentration (Santos et al. 2008), third-order power ratio (Buote & Tsai 1995; Weißmann et al. 2013), centroid shift (Mohr, Fabricant & Geller 1993; O’Hara et al. 2006), strip parameter, Gaussian fit parameter (Cialone et al. 2018), and so on. They exploit the maps with different apertures and efficiencies and are applied individually or combined together, even with different weights (see e.g. Böhringer et al. 2010; Nurgaliev et al. 2013; Rasia et al. 2013; Weißmann et al. 2013; Mantz et al. 2015; Cui et al. 2016; Lovisari et al. 2017; Cialone et al. 2018; Cao et al. 2020; DL20; Yuan & Han 2020). A complementary approach is by applying thresholds on specific thermodynamic variables. Among the others, the central electron gas density and the core entropy are fairly reliable (Hudson et al. 2010). The azimuthal scatter in radial profiles of gas density, temperature, entropy, or surface brightness (Vazza et al. 2011) is also used as a proxy of the ICM inhomogeneities and correlated to the clusters dynamical state (see e.g. Roncarelli et al. 2013; Ansarifard et al. 2020). Alternatively, the projected sky separations between key positions in the images are resulting in reliable estimators of the dynamical state. Interestingly, the offsets between the bright central galaxy (BCG) and the peaks and/or the centroids of X-ray or tSZ maps are an indication of how much the relaxation condition is satisfied, with different efficiency (see e.g. Jones & Forman 1984; Katayama et al. 2003; Lin & Mohr 2004; Sanderson, Edge & Smith 2009; Mann & Ebeling 2012; Rossetti et al. 2016; Lopes et al. 2018; DL20; Ricci et al. 2020; Zenteno et al. 2020). To be mentioned also other approaches based on wavelets analysis (Pierre & Starck 1998), on the Minkowski functionals (Beisbart, Valdarnini & Buchert 2001), or on machine learning (see e.g. Cohn & Battaglia 2019; Green et al. 2019; Gupta & Reichardt 2020).

2018MNRAS.475.4704R__Smith_et_al._2010_Instance_1

Understanding the influence of environment is contingent on being able to identify and quantify galaxy environments. Common environmental measures include the projected number density of galaxies out to the Nth nearest neighbour, the halo mass of a host group or cluster, or the projected separation from the centre of a group or cluster. Star formation and morphology of galaxies correlate well with these environment proxies, with galaxies in high densities regions (or alternatively, high halo mass or small group/cluster-centric radius) being preferentially red, passive, and early type (Dressler 1980; Goto et al. 2003; Poggianti et al. 2008; Kimm et al. 2009; Li, Yee & Ellingson 2009; Wetzel, Tinker & Conroy 2012; Wilman & Erwin 2012; Fasano et al. 2015; Haines et al. 2015). An alternative way to parametrize the environment of a host group or cluster is to classify the degree to which a system is dynamically relaxed. A relaxed, dynamically old group or cluster should be characterized by a central galaxy which is the brightest (most massive) member by a significant margin (e.g. Khosroshahi, Ponman & Jones 2007; Dariush et al. 2010; Smith et al. 2010) and is located near the minimum of the potential well (e.g. George et al. 2012; Zitrin et al. 2012, however also see Skibba et al. 2011), satellite galaxies which are distributed in velocity space according to a Gaussian profile (e.g. Yahil & Vidal 1977; Bird & Beers 1993; Hou et al. 2009; Martínez & Zandivarez 2012), and diffuse X-ray emission which is symmetric about the group/cluster centre (e.g. Rasia, Meneghetti & Ettori 2013; Weißmann et al. 2013; Parekh et al. 2015). The dynamical state of clusters is related to the age of the halo and the time since infall for member galaxies, which simulations have shown is an important quantity in determining the degree to which galaxy properties are affected by environment (e.g. Wetzel et al. 2013; Oman & Hudson 2016; Joshi, Wadsley & Parker 2017). Unrelaxed groups and clusters are systems which formed more recently or which have recently experienced a significant merger event, and in either case it would be expected that the time-since-infall on to the current halo for member galaxies will be relatively short. Therefore galaxies in unrelaxed groups may have properties which have been less influenced by environment compared to galaxies in more relaxed systems.

2021AandA...655A..12T__Tang_et_al._2017b_Instance_2

Using the RADEX3 non local thermodynamic equilibrium (LTE) modeling program (van der Tak et al. 2007) with collisional rate coefficients from Wiesenfeld & Faure (2013), we modeled the relation between the gas kinetic temperature and the measured average of para-H2CO 0.5 × [(322–221 + 321–220)/303–202] ratios, adopting a 2.73 K background temperature, an average observational linewidth of 4.0 km s−1, and column densities N(para-H2CO) = 2.7 × 1012 and 3.7 × 1012 cm−2 for N113 and N159W, respectively. The results are shown in Fig. 5. The values of the para-H2CO column density were obtained with APEX data (beam size ~30″; Tang et al. 2017b), which cover similar regions. Different column densities of para-H2CO only weakly affect derived kinetic temperatures (see Fig. 3 in Tang et al. 2017b or Fig. 4 in Tang et al. 2018a; this was also shown in Fig. 13 and discussed in Sect. 4.3.1 of Mangum & Wootten 1993) as long as all lines are optically thin. Considering that the relation between the gas temperature and the para-H2CO line ratio may vary at different spatial densities (see Fig. 2 in Tang et al. 2017b), we modeled it at spatial densities 104, 105, and 106 cm−3 in Fig. 5. It appears that Tkin at n(H2) = 105 cm−3 is consistently lower than values at 104 and 106 cm−3 by ≲23% and ≲34%, respectively, for Tkin ≲ 100 K. Local thermodynamic equilibrium (LTE) is a good approximation for the H2CO level populations under optically thin and high-density conditions (Mangum & Wootten 1993; Tang et al. 2017a,b, 2018b). Following the method applied by Tang et al. (2017b) in their Eq. (2), we plot the relation between the LTE kinetic temperature, TLTE, and the para-H2CO (3–2) line ratio in Fig. 5. Apparently, TLTE agrees well with Tnon-LTE at volume densities n(H2) ~ 105 cm−3 as long as Tkin ≲ 100 K. Previous observations show that para-H2CO (3–2) is sensitive to gas temperature at density 105 cm−3 (Ginsburg et al. 2016; Immer et al. 2016; Tang et al. 2017b). The spatial density measured with para-H2CO (303–202) and C18O (2–1) in N113 and N159W is n(H2) ~ 105 cm−3 on a size of ~30″ (Tang et al. 2017b). Therefore, here we adopt 105 cm−3 as an averaged spatial gas density in the N113 and N159W regions.

2019AandA...626A.130T__Imanishi_et_al._(2007)_Instance_1

We detect a deep (τsil  ∼  2.3) absorption feature due to silicate grains at around 10 μm. We compared the optical depth of the silicate feature in LEDA 1712304 with those in other AGNs in a wide range of IR luminosities (1010 L⊙   LIR   1013 L⊙). The spectra of the AGNs to be compared are taken from those of the IR galaxies observed by Spitzer/IRS (Stierwalt et al. 2013; Imanishi et al. 2007; Imanishi 2009; Roussel et al. 2006) with the threshold that the equivalent width of the PAH 6.2 μm feature is smaller than 0.27 μm (Stierwalt et al. 2013). In addition, we also take AGNs with low IR luminosities (LIR   1011 L⊙) from Wu et al. (2010). Figure 3 shows the relation between the IR luminosity and the optical depth of the silicate feature for LEDA 1712304 and the AGNs. We estimated the optical depths of the AGNs of Stierwalt et al. (2013), Wu et al. (2010), and Roussel et al. (2006) by ourselves in the same manner as was performed for LEDA 1712304 with the method defined by Imanishi et al. (2007), the spectra of which were obtained from the NASA/IPAC IR Science Archive (IRSA). On the other hand, we adopted the values given in each reference paper for the other AGNs. The blue dotted lines in Fig. 3 show the differences between our estimates and those by Stierwalt et al. (2013), which confirms that the differences between the different methods are not large enough to change a global relation. Figure 3 shows that galaxies with low IR luminosities (LIR   1011 L⊙) show significantly shallow silicate absorption features, as already pointed out by Stierwalt et al. (2013). Imanishi (2009) suggested that the number of heavily obscured AGNs that have deep silicate absorption features (τsil >  2) increases with the IR luminosities of the host galaxies. Therefore, LEDA 1712304 may be a rare galaxy from the aspect of having both deep absorption feature τsil ∼ 2.3 and low IR luminosity (5 ± 1)×1010 L⊙. Such galaxies have hardly been observed; an exception is NGC 1377 (Roussel et al. 2006), as can be seen in Fig. 3. NGC 1377 is a lenticular galaxy (de Vaucouleurs et al. 1991), the stellar mass of which is 109.3 ± 0.1 M⊙ (Skibba et al. 2011). The IR spectrum of NGC 1377 shows a featureless continuum except the silicate feature due to circumnuclear dust (Imanishi 2006; Roussel et al. 2006).

2020AandA...641A..29G__Kotera_et_al._(2015)_Instance_1

Our case studies focus on several regions of the parameter space of explosive transients that may be related to specific source categories. From Sect. 3, different types of transient emissions from highly magnetized pulsars (also magnetars) can be affected by secondary acceleration. As mentioned previously, magnetars have been identified in many studies as promising candidates for the acceleration of cosmic rays and the production of secondary high-energy neutrinos, for instance Blasi et al. (2000), Fang et al. (2012, 2013), Lemoine et al. (2015), Kotera et al. (2015). The case of newborn magnetars with millisecond periods illustrates a non-relativistic source class, whereas the case of magnetar giant flares involves relativistic outflows. Moreover, tidal disruption events, low-luminosity gamma-ray bursts and blazar flares are examples of relativistic outflows, whose properties partially overlap in the parameter space of explosive transients. In these overlapping regions, they can be similarly affected by secondary acceleration. Therefore, we chose to describe the case of jetted tidal disruptions, while keeping in mind that this case study can be used as a benchmark example for low-luminosity gamma-ray bursts and blazar flares. We note that beyond standard scenarios involving gamma-ray bursts (e.g., Waxman & Bahcall 1997; Murase & Nagataki 2006; Murase et al. 2008; Mészáros 2015) and active galactic nuclei (e.g., Bednarek & Protheroe 1999; Atoyan & Dermer 2001; Halzen & Hooper 2005; Dermer et al. 2014; Petropoulou et al. 2016; Murase et al. 2018; Gao et al. 2019), jetted tidal disruptions have also been proposed as candidate sources for the production of high-energy cosmic rays and neutrinos (Wang et al. 2011; Senno et al. 2016; Dai & Fang 2017; Lunardini & Winter 2017; Wang & Liu 2016; Zhang et al. 2017; Biehl et al. 2018; Guépin et al. 2018). These case studies are associated with different types of photon fields that we simply model by hard or soft broken power laws and we can thus assess their impact on the high-energy neutrino spectrum. From Sect. 3, we can see that all these source categories are affected by strong secondary synchrotron losses and should be affected differently by secondary acceleration.

2019AandA...626A.130T__Imanishi_et_al._(2007)_Instance_2

We detect a deep (τsil  ∼  2.3) absorption feature due to silicate grains at around 10 μm. We compared the optical depth of the silicate feature in LEDA 1712304 with those in other AGNs in a wide range of IR luminosities (1010 L⊙   LIR   1013 L⊙). The spectra of the AGNs to be compared are taken from those of the IR galaxies observed by Spitzer/IRS (Stierwalt et al. 2013; Imanishi et al. 2007; Imanishi 2009; Roussel et al. 2006) with the threshold that the equivalent width of the PAH 6.2 μm feature is smaller than 0.27 μm (Stierwalt et al. 2013). In addition, we also take AGNs with low IR luminosities (LIR   1011 L⊙) from Wu et al. (2010). Figure 3 shows the relation between the IR luminosity and the optical depth of the silicate feature for LEDA 1712304 and the AGNs. We estimated the optical depths of the AGNs of Stierwalt et al. (2013), Wu et al. (2010), and Roussel et al. (2006) by ourselves in the same manner as was performed for LEDA 1712304 with the method defined by Imanishi et al. (2007), the spectra of which were obtained from the NASA/IPAC IR Science Archive (IRSA). On the other hand, we adopted the values given in each reference paper for the other AGNs. The blue dotted lines in Fig. 3 show the differences between our estimates and those by Stierwalt et al. (2013), which confirms that the differences between the different methods are not large enough to change a global relation. Figure 3 shows that galaxies with low IR luminosities (LIR   1011 L⊙) show significantly shallow silicate absorption features, as already pointed out by Stierwalt et al. (2013). Imanishi (2009) suggested that the number of heavily obscured AGNs that have deep silicate absorption features (τsil >  2) increases with the IR luminosities of the host galaxies. Therefore, LEDA 1712304 may be a rare galaxy from the aspect of having both deep absorption feature τsil ∼ 2.3 and low IR luminosity (5 ± 1)×1010 L⊙. Such galaxies have hardly been observed; an exception is NGC 1377 (Roussel et al. 2006), as can be seen in Fig. 3. NGC 1377 is a lenticular galaxy (de Vaucouleurs et al. 1991), the stellar mass of which is 109.3 ± 0.1 M⊙ (Skibba et al. 2011). The IR spectrum of NGC 1377 shows a featureless continuum except the silicate feature due to circumnuclear dust (Imanishi 2006; Roussel et al. 2006).

2021MNRAS.507.6012Z__Kendrick,_Hazra_&_Balakrishnan_2015_Instance_1

The interaction of H2 and HD with atomic hydrogen is among the most widely investigated and important processes in elementary chemical reactions. The H + H2, H + D2, and H + HD reactions serve as benchmarks for experimental and theoretical investigations of bimolecular processes (Marinero, Rettner & Zare 1984; Zhang & Miller 1989; D’Mello, Manolopoulos & Wyatt 1991; Fernández-Alonso & Zare 2002; Harich et al. 2002; Aoiz, Bañares & Herrero 2005; Yang 2007; Gao et al. 2015; Karandashev et al. 2017; Yuan et al. 2018a,b, 2020; Goswami et al. 2020) and continue to attract much attention in the quest for unraveling quantum effects such as the geometric phase (GP) in chemical reactions (Kendrick, Hazra & Balakrishnan 2015; Hazra, Kendrick & Balakrishnan 2016; Croft et al. 2017; Kendrick 2018; Yuan et al. 2018a,b; Kendrick 2019). These elementary reactions are also of considerable interest in early universe chemistry models in determining H2 and HD column densities and the relative abundance of H/D in the interstellar medium (Flower 1999, 2000; Flower & Roueff 1999; Neufeld et al. 2006; Wrathmall, Gusdorf & Flower 2007; Gay et al. 2011; Nolte et al. 2011; Desrousseaux et al. 2018; Walker, Porter & Stancil 2018; Neufeld et al. 2019; Zhou et al. 2020). The H + HD ↔ D + H2 chemical reaction is especially important in this context as it cycles the heavier isotope between molecular form (HD) and purely atomic form. The HD molecule by virtue of its small dipole moment also serves as a tracer of H2 through its j = 1 → 0 rotational transition at 112 μm (Neufeld et al. 2006; Desrousseaux et al. 2018). Observations (Howat et al. 2002; Yuan et al. 2012) using the Infrared Spectrograph on the Spitzer Space Telescope have reported HD emissions from excited rovibrational level v = 1, j = 5, as well as the pure rotational R(3) and R(4) lines, but relevant reaction rate coefficients are still limited in terms of initial HD rovibrational levels and gas temperature.

2022MNRAS.516..731B__Wang,_Hammer_&_Yang_2022_Instance_1

Now with the recent availability of high quality full 6D phase-space information for large numbers of sources, much effort has been made to decrease the uncertainties in the Milky Way mass estimate. Recent works using a tracer mass estimator with 6D phase-space information include Sohn et al. (2018, globular clusters), Watkins et al. (2019, globular clusters), and Fritz et al. (2020, satellites). The most recent work using the spherical Jeans equation by Zhai et al. (2018) is very similar to our current investigation in method and data (LAMOST K giants) although only line-of-sight velocities were included, whereas we additionally make use of proper motions from Gaia to obtain the stellar tangential velocities. Using Bayesian analysis to fit a distribution function to full 6D phase-space data (globular clusters, satellites, and halo stars) has been a recent popular choice among many works (Eadie & Jurić 2019; Posti & Helmi 2019; Vasiliev 2019; Deason et al. 2021; Correa Magnus & Vasiliev 2022; Shen et al. 2022; Slizewski et al. 2022; Wang, Hammer & Yang 2022) and a similar distribution function analysis using 5D phase-space data from Gaia (Hattori, Valluri & Vasiliev 2021). In addition to fitting the observational data with a distribution function, several works have incorporated into the fitting a comparison of the observed data with Milky Way-type galaxies from cosmological simulations (Callingham et al. 2019; Li et al. 2020). Newly discovered high velocity stars with full 6D phase-space information have been used to estimate the mass of the Milky Way (Hattori et al. 2018; Monari et al. 2018; Deason et al. 2019; Grand et al. 2019; Koppelman & Helmi 2021; Necib & Lin 2022). Vasiliev, Belokurov & Erkal (2021) and Craig et al. (2021) have estimated the Milky Way mass by fitting models for the Sagittarius and Magellanic Steams, respectively. Several recent studies have estimated the Milky Way mass using measurements of the rotation curve (de Salas et al. 2019; Eilers et al. 2019; Ablimit et al. 2020; Cautun et al. 2020; Karukes et al. 2020; Jiao et al. 2021). Other works have used 6D satellite phenomenology, characterizing simulated Milky Way-type satellite populations and comparing to the observations of satellites in the Milky Way, to estimate the mass of the Milky Way (Patel et al. 2018; Villanueva-Domingo et al. 2021; Rodriguez Wimberly et al. 2022). Zaritsky et al. (2020) apply the timing argument to distant Milky Way halo stars to derive a lower limit to the Milky Way mass.

2018MNRAS.475.1646F__Ji_et_al._2003_Instance_1

Solar filaments, or prominences as they are called when observed above the solar limb, can be observed in a stable state for many days or weeks. Sometimes, they suddenly start to ascend as a whole (full eruptions) (Joshi & Srivastava 2011; Holman & Foord 2015) or within limited sections of their length (partial eruptions) (Gibson & Fan 2006; Kliem et al. 2014). The ascending of a filament can go on high into the corona (successful eruptions) and gives rise to a coronal mass ejection (CME) or can stop at some greater height in the corona (confined or failed eruptions) (Ji et al. 2003; Török & Kliem 2005; Alexander, Liu & Gilbert 2006; Kuridze et al. 2013; Kushwaha et al. 2015). Occasionally two-step eruptions are observed. A filament after the first jump decelerates and stops at a greater height as in failed eruptions, but after a rather short period of time it starts to rise again and develops into the successful eruption with a CME formation. Byrne et al. (2014) observed on 2011 March 8 at the solar limb the erupting loop system that stayed in a metastable intermediate position for an hour and then proceeded and formed the core of a CME. Gosain et al. (2016) analysed observations of the eruption of a long quiescent filament on 2011 October 22 observed from three viewpoints by space observatories. A two-ribbon flare and the onset of a CME appeared 15 h after the filament disappearance on the disc. The filament was not observed at the high metastable position but some coronal structures that can be attributed to a corresponding flux rope were recognized. A clear example of the two-step filament eruption on 2015 March 14–15 was reported by Wang et al. (2016) and Chandra et al. (2017). In this event, a part of a large filament separated from the main body of the filament at the height of ≈30 Mm and rose upwards to the height of ≈80 Mm, where it stayed for 12 h clearly visible in chromospheric and coronal spectral lines. Finally, it erupted and produced a halo CME.

2018ApJ...864..165W__Golub_et_al._1974_Instance_1

In configuration 1S, the field close to the separatrix was sheared, producing steady interchange reconnection modulated by quasi-periodic reconnection bursts. We can roughly estimate the free energy release rate of the steady component from the energy injected before the onset of reconnection in the first 200 time units of the simulation. By t = 200, around 2 units of free magnetic energy are injected into the closed field; see Figure 16(a). Accounting for the ramp up of the driver and scaling the values, this corresponds to an energy injection rate of ≈5.6 × 1023 erg s−1 at the maximum driving speed. During the quasi-steady phase, this injection is balanced by losses to numerical diffusion and equates to roughly the free energy available for heating the plasma. Even after accounting for the unrealistically fast driving speed (see below), this energy release rate compares well with the observed values of 1023–1024 erg s−1 for bright points (Golub et al. 1974; Priest et al. 1994). The energy released by the bursts was a small fraction of the stored free magnetic energy—≈0.5 × Es = 4.9 × 1025 erg occurring with a period of ≈240 × ts = 8 minutes—while the outflow speeds reached typical values of ≈0.05 × Vs ≈ 60 km s−1 along the outer spine. The energy released in each burst corresponds to ≈18% of the energy released over the same period by the steady component. Many bright points exhibit quasi-periodic intensity increases, with periods ranging from a few minutes to a couple of hours (Kariyappa & Varghese 2008; Tian et al. 2008; Zhang et al. 2012). Our results demonstrate that some of this periodicity can be explained by the natural modulation of the interchange reconnection that occurs as minority-polarity elements are moved by surface motions. The predicted outflow speeds, and certainly the periods of the reconnection cycles, are likely too fast because the driving speed (12.5 km s−1) employed in our simulations is too high. However, configuration 1F demonstrated that the cycle period is mainly set by the displacement of the minority polarity. We speculate that, at more typical photospheric speeds (≈1.5 km s−1; e.g., Brandt et al. 1988), the reconnection cycle period would increase by a factor of 12.5/1.5 × 8 minutes ≈67 minutes, corresponding to the longest observed oscillations in brightness. Without a full treatment of the thermodynamics, however, it is not clear whether the repetitive, low-intensity reconnection jets in this case would be observable.

2020ApJ...899L...6L__Margalit_et_al._2019_Instance_1

The leading FRB source model invokes magnetars as the power source to produce repeating bursts. There are two versions of this model. One version invokes rapidly spinning young magnetars that are produced in extreme stellar transients such as GRBs and SLSNe. The main motivation is that the host galaxy of FRB 121102 resembles those of LGRBs and SLSNe (Metzger et al. 2017; Nicholl et al. 2017; Wadiasingh & Timokhin 2019). The fact that the hosts of all other FRBs do not resemble that of FRB 121102 disfavors the simplest version of this proposal. A possible fix of this proposal is to introduce rapidly spinning magnetars born from binary neutron star (BNS) mergers (Margalit et al. 2019; Wang et al. 2020). In order to make this scenario work, one needs to require that rapidly spinning magnetars made from BNS mergers should be much more abundant than those made from LGRBs and SLSNe. Comparing the event rate densities of BNS mergers, LGRBs, and SLSNe (e.g., Sun et al. 2015; Abbott et al. 2017; Nicholl et al. 2017), this may be possible if a significant fraction of BNS mergers leave behind stable neutron stars (e.g., Gao et al. 2016). However, if this fraction is very low, as required if GW170817 leaves behind a black hole (Margalit et al. 2019), the fast magnetar model may fail to explain the small fraction of LGRB/SLSN-like hosts in FRB samples. The second version of the magnetar model invokes emission (e.g., giant flares) from slowly rotating magnetars like the ones observed in the Galaxy (e.g., Popov & Postnov 2010; Katz 2014; Kulkarni et al. 2014). The births of these magnetars do not require extreme explosions such as GRBs and SLSNe (e.g., Beniamini et al. 2019). If this is the case, the host galaxy distribution may be more analogous to that of SNe II. All FRBs but FRB 121102 are consistent with this scenario (Figure 4). In order to interpret FRB 121102, the more extreme channel of forming rapid magnetars is still needed. So we conclude that the magnetar model would work only if both fast magnetars produced in extreme explosions and slow magnetars produced in regular channels (Beniamini et al. 2019) can produce FRBs. In any case, since the birth rate of these magnetars is very high (Beniamini et al. 2019), an additional factor is needed to select a small fraction of magnetars to produce FRBs (e.g., Ioka & Zhang 2020).

2021MNRAS.503.3629D__Kelley,_Blecha_&_Hernquist_2017a_Instance_1

Having found evidence that galaxies show morphological signatures of a recent galaxy merger over time-scales on the order of 300–500 Myr, we now consider the typical BH merger time-scales and the impact that may have on our results. Within the Illustris simulation (and indeed many similar simulations), a pair of BHs merge as soon as their separation is less than the particle’s smoothing length, rather than incorporating a coalescence time for the binary. Recently, several works have attempted to estimate the expected coalescence time for binary BHs by post-processing cosmological simulations, finding time-scales on the order of 100s of Myr to Gyr for the binary coalescence time (e.g. Blecha et al. 2016; Rantala et al. 2017; Kelley, Blecha & Hernquist 2017a; Mannerkoski et al. 2019; Sayeb et al. 2021). Additionally, the time for a satellite BH to reach the centre of a galaxy and form a binary with the central BH can also be on the order of 100s of Myr to Gyr, based on the dynamical friction time-scale for infall to the galactic centre (e.g. Volonteri et al. 2020). The galaxy structure, e.g. the existence or lack of a dense stellar core, can affect the time satellite BHs spend at large radii (Tremmel et al. 2018a,b; Barausse et al. 2020), and directly incorporating dynamical friction into cosmological volumes suggests that the orbital decay time-scale may be both substantial and redshift dependent (Bartlett et al. 2021). The Illustris simulation uses a re-centring scheme whereby BHs are re-positioned towards the local potential minimum; this prevents numerical wandering of BHs, but also means the the full infall time to reach the galaxy centre may be notably underestimated. Furthermore, both simulations (e.g. Bellovary et al. 2019) and observations (e.g. Reines et al. 2020) suggest that BHs in dwarf galaxies may frequently be located offset from the galaxy centre, which could further delay any mergers involving low-mass BHs seeded into Illustris (which are initially placed at the galaxy centre). Overall, this suggests that the Illustris simulation likely overestimates the speed with which BHs merge following the merging of their host galaxies, and properly accounting for this has the potential to impact the expected GW detection rate, shift the peak detection time to lower redshift, and prevent GW hosts from being visibly disturbed. Although a complete investigation into accurately estimating the time delays remains beyond the scope of this paper, we address this, by imposing a delay between when the BH particles merge in the simulation (which is closer to when the BH binary may form) and when the final coalescence and GW emission occur.

2016ApJ...821..107G__Gloeckler_&_Fisk_2015_Instance_2

We repeated the plasma pressure calculation presented by Schwadron et al. (2011) and Fuselier et al. (2012) for the new ENA energy spectrum. The results for the downwind hemisphere and for the Voyager 1 region are summarized in Table 3. The measured intensity j ENA of neutralized hydrogen at a given energy translates into a pressure of the parent ion population in the heliosheath times the integration length along the line of sight, ΔP × l, in the following way: 3 Δ P × l = 4 π 3 n H m H v j ENA ( E ) σ ( E ) Δ E c f 4 c f = ( v + u R ) 2 v 4 ( v 2 + 4 u R 2 + 2 u R v ) . In Equation (3), ΔE denotes the width of the respective energy bin; for the typical radial velocity of solar wind in the flanks and the downwind hemisphere of the inner heliosheath, we assumed uR = 140 km s−1 as measured by Voyager 2, whereas uR = 40 km s−1 for the heliosheath in the Voyager 1 direction (Schwadron et al. 2011; Gloeckler & Fisk 2015). For the density of neutral hydrogen in the inner heliosheath a constant nH = 0.1 cm−3 is assumed (Schwadron et al. 2011; Gloeckler & Fisk 2015). The charge-exchange cross section between protons and neutral hydrogen decreases from (4 to 2) × 10−15 cm−2 for 0.015 to 1.821 keV (Lindsay & Stebbings 2005). The integration length l for ENA production in the plasma is approximately the thickness of the inner heliosheath. The part of Equation (3) without the velocity factor cf can be interpreted as stationary pressure. The total pressure or dynamic pressure is the stationary pressure times this factor. Integrating over all energy bins in Table 3, we obtain the total plasma pressure times heliosheath thickness as P × l = 304 pdyn cm−2 au for the downwind hemisphere and 66 pdyn cm−2 au for the Voyager 1 region (1 pdyn cm−2 au = 0.015 N m−1). If we want to put these numbers into the context of other studies, we face two problems. First, the uncertainty of the total pressure is large given the upper limits in the two lowest energy bins. Second, heliosheath plasma more energetic than 2 keV will produce ENAs that cannot be detected with IBEX-Lo. We therefore used the observed median j = 0 cm−2 sr−1 s−1 keV−1 for heliospheric ENAs in the two lowest energy bins of IBEX-Lo and relied on the study by Livadiotis et al. (2013). They compared the expected plasma pressure from a kappa distribution of protons with the plasma pressure derived from IBEX-Hi energy spectra: the energy range between 0.03 and 2 keV, roughly corresponding to the IBEX-Lo range, covered more than half of the total plasma pressure predicted from a kappa distribution. The authors found a total plasma pressure of P = 2.1 pdyn cm−2 for all sky directions except for the ENA Ribbon. Gloeckler & Fisk (2015) presented a multi-component plasma model for the heliosheath to explain Voyager and IBEX observations. At low energies they assumed the ENA energy spectra provided by Fuselier et al. (2012). They derived a total pressure of 2.5 pdyn cm−2 in all three plasma regions in the nose of the heliotail (Gloeckler & Fisk 2015). Pressure contributions from the slowed solar wind, magnetic pressure, and the pressure exerted from pickup ions and anomalous cosmic rays all had to be taken into account to obtain this total pressure.

2018ApJ...853...34Z__Giebels_et_al._2007_Instance_1

Several well-studied TeV blazars show rich spectral behavior in X-rays, which may represent the general behavior of the synchrotron peak of all AGN jets. The X-ray spectra are usually curved (Massaro et al. 2004) and can only locally be fitted by a power law. The spectral variation with flux can be complex (Zhang et al. 2002; Cui 2004). Generally, the spectrum hardens when the flux increases (e.g., Gliozzi et al. 2006; Xue et al. 2006; Tramacere et al. 2009), but photon indices can saturate at higher fluxes (Xue & Cui 2005; Giebels et al. 2007). The synchrotron peak usually moves to higher frequencies with increasing flux during outbursts (e.g., Pian et al. 1998), but no correlation between the break energy and the flux exists when a broken power law is adopted to fit the X-ray spectra (Xue & Cui 2005; Giebels et al. 2007; Garson et al. 2010). A cooling break in the spectrum of emitting particles cannot explain these features (Wierzcholska & Wagner 2016), and some special particle acceleration processes may be involved (Madejski & Sikora 2016). There are also energy-dependent lags between the variations of different energy bands. In some flares, soft bands lag behind hard bands (e.g., Zhang et al. 2002), while lags in the opposite direction can also happen (e.g., Ravasio et al. 2004; Sato et al. 2008). Hysteresis in the HR (hardness ratio)–flux diagram is often used as a diagnostic of lags. Clockwise loops (e.g., Acciari et al. 2009; Kapanadze et al. 2016) in the HR–flux plane are a sign of soft lags while counterclockwise loops (e.g., Tramacere et al. 2009) are a sign of hard lags. The same source can exhibit both clockwise and counterclockwise loops; the observed patterns are further complicated by the superposition of flares at different timescales (Cui 2004). The above knowledge of TeV blazars in the X-ray regime comes from studies focusing on timescales of hours to weeks. We will extend this kind of analysis to much smaller timescales in this paper.

2022AandA...665A..46M__Côté_et_al._2019_Instance_1

Several nucleosynthesis processes have been proposed as production sites of these light neutron-capture elements, including r-process in neutron star mergers (Wanajo et al. 2014; Watson et al. 2019), in magneto-rotational supernovae (Winteler et al. 2012), or in collapsars (Siegel et al. 2019), s-process in low- to intermediate-mass stars (Karakas & Lattanzio 2014), weak s-process in rapidly rotating massive stars (Frischknecht et al. 2012; Choplin et al. 2018), and weak r-process in electron-capture supernovae (Wanajo et al. 2011). It is not yet clear which process is the dominant source of the elements in the early Universe (see discussions by, e.g., Côté et al. 2019; Prantzos et al. 2018; Kobayashi et al. 2020). Since we here discuss the low-metallicity end of the sample, the production of neutron-capture elements would not be dominated by low- to intermediate-mass stars (e.g., de los Reyes et al. 2022). One possible explanation for the low light neutron-capture element abundances of the Helmi streams is that, as a result of the low stellar mass of the galaxy, the progenitor did not experience rare r-process nucleosynthesis events, such as neutron star mergers, electron capture supernovae, and magneto-rotational supernovae. In this case, a small amount of light neutron-capture elements could be produced by rapidly rotating massive stars (Hirai et al. 2019; Tarumi et al. 2021). Another explanation is that the progenitor dwarf galaxy had a small number of rotating massive stars. The small number of rotating massive stars might be a result of the top-light initial mass function in dwarf galaxies (Weidner & Kroupa 2005), or different distribution of initial rotation velocity of stars. The observational indication by Gull et al. (2021) that metal-poor stars of the Helmi streams show r-process abundance pattern in neutron-capture elements heavier than Ba might favor the second possibility. However, it is necessary to investigate the abundance pattern of light neutron-capture elements in order to understand the cause of the low light neutron-capture element abundance of the Helmi streams. A larger sample of low-metallicity Helmi stream stars with neutron-capture element abundances would also be welcomed. They would enable us to constrain the property of the nucleosynthesis processes, such as their event rates, by studying how neutron-capture elements were enriched as a function of metallicity (e.g., Tsujimoto et al. 2017).

2022MNRAS.513.1459M__Conselice,_Yang_&_Bluck_2009_Instance_1

Hierarchical structure formation scenarios (e.g. Fall & Efstathiou 1980; van den Bosch et al. 2002; Agertz, Teyssier & Moore 2011) predict that massive galaxies acquire much of their stellar mass through a combination of continuous cold gas accretion and mergers with smaller objects (e.g. Press & Schechter 1974; Moster, Naab & White 2013; Kaviraj et al. 2015; Rodriguez-Gomez et al. 2016; Martin et al. 2018b; Davison et al. 2020; Martin et al. 2021). As a consequence, mergers are also expected to play a significant role in driving the evolution of galaxy properties, for example, by triggering (Schweizer 1982; Mihos & Hernquist 1996; Duc et al. 1997; Elbaz & Cesarsky 2003; Kaviraj et al. 2011; Lofthouse et al. 2017; Martin et al. 2017) or quenching (Schawinski et al. 2014; Barro et al. 2017; Kawinwanichakij et al. 2017; Pontzen et al. 2017) star formation in the host galaxy or by driving its morphological evolution (e.g. Toomre 1977; Conselice, Yang & Bluck 2009; Dekel, Sari & Ceverino 2009; Taranu, Dubinski & Yee 2013; Naab et al. 2014; Fiacconi, Feldmann & Mayer 2015; Graham, Dullo & Savorgnan 2015; Deeley et al. 2017; Gómez et al. 2017; Welker et al. 2017; Martin et al. 2018a; Jackson et al. 2019). Signatures of past mergers take the form of faint extended tidal features such as tails (e.g. Pfleiderer 1963; Toomre & Toomre 1972; Peirani et al. 2010; Kaviraj 2014; Kaviraj, Martin & Silk 2019), or plumes (e.g. Lauer 1988) – which are typically produced by major mergers – and streams (e.g. Johnston, Sigurdsson & Hernquist 1999; Shipp et al. 2018; Martinez-Delgado et al. 2021) or shells (e.g. Malin & Carter 1983; Quinn 1984) – which mainly arise from minor interactions – as well as in the structure of the surrounding diffuse light (e.g. Choi, Guhathakurta & Johnston 2002; Graham 2002; Johnston, Choi & Guhathakurta 2002; Seigar, Graham & Jerjen 2007; Kaviraj et al. 2012; Monachesi et al. 2016, 2019; Iodice et al. 2019; Montes 2019). These features, which arise from many different types of encounter, hold a fossil record of the host galaxy’s past interactions and mergers which can be used to reconstruct its assembly history and dynamical history (Johnston et al. 2008; Martínez-Delgado et al. 2009; Belokurov et al. 2017; Montes et al. 2020; Ren et al. 2020; Spavone et al. 2020; Vera-Casanova et al. 2021). However, the majority of tidal features are expected to have surface brightnesses fainter than 30 mag arcsec−2 in the r-band (Johnston et al. 2008). Although pushing towards these kinds of limiting surface brightnesses remains extremely challenging, it is nevertheless desirable to do so, being necessary to uncover a more detailed history of local Universe. This is not only vital for our understanding of hierarchical galaxy assembly (e.g. Johnston, Sackett & Bullock 2001; Wang et al. 2012), but also serves as a novel galactic scale probe of more fundamental physics such as theories of gravity (e.g. Gentile et al. 2007; Renaud, Famaey & Kroupa 2016) and dark matter (Dubinski, Mihos & Hernquist 1996; Kesden & Kamionkowski 2006; Dumas et al. 2015; van Dokkum et al. 2018; Montes et al. 2020). In particular, tidal structure is a powerful tracer of the underlying galactic halo potential (e.g. Dubinski, Mihos & Hernquist 1999; Varghese, Ibata & Lewis 2011; Bovy et al. 2016; Ibata et al. 2020; Malhan, Valluri & Freese 2021).

2019MNRAS.486....2M__Yamamoto_et_al._2014_Instance_1

The discovery of ultraluminous pulsars (ULPs or PULXs; Bachetti et al. 2014; Fürst et al. 2016; Israel et al. 2017a,b; Carpano et al. 2018) has revolutionised the field of ultraluminous X-ray sources (ULXs; see the review of Kaaret, Feng & Roberts 2017). Whilst ULXs were long considered to be possible candidates for hosting intermediate-mass black holes (IMBHs), it was immediately apparent that the explanation for the extreme observed luminosities (>1039 erg s−1) in at least some ULXs was accretion in excess of the classical Eddington limit on to common-place primary objects – in this case neutron stars. However, whilst the mass regime of the compact object in ULXs has been at least partly resolved (we note that candidate IMBHs still remain, e.g. Farrell et al. 2009), the relative number of black hole to neutron star primaries in ULXs (see King, Lasota & Kluźniak 2017; Middleton & King 2017) and the nature of the accretion flow in ULPs remain outstanding puzzles. At the centre of the debate is the strength of the surface dipole field and any multipolar component. Should the dipole field strength be similar to that of Galactic HMXBs (1012 G – e.g. Bellm et al. 2014; Fürst et al. 2014; Tendulkar et al. 2014; Yamamoto et al. 2014) then it is quite plausible that the flow will be supercritical ($\dot{m}/\dot{m}_{\rm Edd} \gt $ 1 where $\dot{m}_{\rm Edd}$ is the Eddington accretion rate) at radii greater than the magnetospheric truncation radius (rM). In this case, the supercritical portion of the disc will have a large – close to unity – vertical scale height and winds will be launched from the surface (see Shakura & Sunyaev 1973; Poutanen et al. 2007 and the simulations of Ohsuga et al. 2009; Jiang, Stone & Davis 2014; Sa̧dowski et al. 2014). Within rM, the flow will take the form of an accretion curtain (Mushtukov et al. 2017, 2019) and shock-heated column as material falls on to the magnetic poles. Due to collimation by the disc and outflows beyond rM, it is expected that the intrinsic luminosity is then partially geometrically beamed (see King 2009). Conversely, should the dipole field strength be very high (typically > 1013 G) then it is quite probable that the disc will truncate before becoming locally supercritical. The geometry in this case is then expected to take the form of a geometrically thin disc down to rM, an accretion curtain and shock-heated column. Rather than a supercritical disc and geometrical beaming, super-Eddington luminosities can then be explained by a magnetic pressure supported accretion column (Basko & Sunyaev 1976) and high field strength, the latter allowing for a substantially increased luminosity from a reduction in the electron scattering cross-section (e.g. Herold 1979; Paczynski 1992; Thompson & Duncan 1995; Mushtukov et al. 2015).

2016AandA...593A..22R__Shibuya_et_al._2015_Instance_2

Although it is a simple concept, obtaining galaxy sizes is not an easy task and is subject to a number of assumptions. The most common way to derive galaxy sizes is by performing light-profile fitting assuming a given shape of the surface brightness profile using a χ2 minimization (e.g. Simard et al. 1999; Peng et al. 2002; Ravindranath et al. 2004; Daddi et al. 2005; Ravindranath et al. 2006; Trujillo et al. 2006; Akiyama et al. 2008; Franx et al. 2008; Tasca et al. 2009; Cassata et al. 2010, 2013; Williams et al. 2010; Mosleh et al. 2011; Huang et al. 2013; Ono et al. 2013; Stott et al. 2013; Morishita et al. 2014; van der Wel et al. 2014; Straatman et al. 2015; Shibuya et al. 2015). Another method assumes circular or elliptical apertures around a predefined galactic center and computes the size enclosing a given percentage of the total galaxy flux (e.g. Ferguson et al. 2004; Bouwens et al. 2004; Hathi et al. 2008; Oesch et al. 2010; Ichikawa et al. 2012; Curtis-Lake et al. 2016). A third approach, involving counting the number of pixels belonging to the galaxy to derive its size, was also explored in Law et al. (2007). Studies of galaxy sizes at z> 2 became possible with the deep imaging obtained with HST. The first reports on size evolution found that galaxy sizes as observed in the UV rest-frame were becoming smaller at the highest redshifts (Bouwens et al. 2003, 2004; Ferguson et al. 2004). We have now access to the size evolution up to z ~ 10 from the deepest HST imaging data (e.g., Hathi et al. 2008; Jiang et al. 2013; Ono et al. 2013; Kawamata et al. 2015; Holwerda et al. 2015; Shibuya et al. 2015). With the multiwavelength and near-infrared coverage of CANDELS (Grogin et al. 2011; Koekemoer et al. 2011) optical rest-frame measurements are reported up to z ~ 3 for a large collection of galaxies in diverse populations (e.g. Bruce et al. 2012; van der Wel et al. 2014; Morishita et al. 2014). At z ~ 2 the size of star-forming galaxies (SFGs) is, to first order, independent of the observed rest-frame bands (Shibuya et al. 2015). It is generally accepted that galaxy sizes tend to decrease with increasing redshift (e.g. Bouwens et al. 2003, 2004; Ferguson et al. 2004; Mosleh et al. 2012) and that galaxy sizes depend on stellar mass (e.g. Franx et al. 2008; van der Wel et al. 2014; Morishita et al. 2014) and luminosity (e.g. Grazian et al. 2012; Huang et al. 2013). However, some results point to a scenario consistent with no size evolution as seen in UV rest-frame from HST data (Law et al. 2007; Curtis-Lake et al. 2016) and, at a fixed stellar mass, from optical rest-frame ground-based data (Ichikawa et al. 2012; Stott et al. 2013).

2021ApJ...922..131T__Kumari_et_al._2019_Instance_1

DIG contributes with a fraction of 20% to 90% of the total Hα flux in galaxy disks, with a mean fraction around 50%–60% (Hoopes & Walterbos 2003; Oey et al. 2007; Sanders et al. 2017; Tomičić et al. 2017; Poetrodjojo et al. 2019; Della Bruna et al. 2020; Tomičić et al. 2021). This large contribution may cause star formation rates (SFRs) to be overestimated as Hα flux from DIG may be wrongly associated with star formation. There is a debate about the extent to which DIG affects measurements of gas-phase metallicity and its radial slope (Searle 1971; Vila-Costas & Edmunds 1992; Sánchez et al. 2014; Belfiore et al. 2017b; Sanders et al. 2017; Sánchez-Menguiano et al. 2018; Zhang et al. 2017; Vale Asari et al.2019; Kumari et al. 2019; Poetrodjojo et al. 2019) as some observations indicate lower metallicity (up to 1 dex) in DIG compared to nearby H ii regions. DIG may also exhibit different values of line ratios and ionizing parameter log(q), further affecting observations and analysis of ISM characteristics as well as adding scatter in the distribution of galaxy properties measured from unresolved observations (Martin 1997; Flores-Fajardo et al. 2011; Dopita et al. 2014; Zhang et al. 2017; Poetrodjojo et al. 2018; Mingozzi et al. 2020). Furthermore, the detection of gas that shows different line ratios and ionization parameters located at large distances from H ii regions—larger than the thickness of a typical galactic disk ( ≈ 1 kpc)—would indicate that sources other than star-forming (SF) regions are ionizing such gas (for example HOLMES, shocks, or mixing of different gas layers; Flores-Fajardo et al. 2011; Zhang et al. 2017; Poetrodjojo et al. 2018; Poggianti et al. 2019a). Different galactic characteristics (like mass, SFR, age, etc.) and external physical processes, such as galaxy interactions and gas stripping caused by ram pressure (Gunn et al. 1972; Toomre & Toomre 1972), may affect the ionization parameter and various line ratios (Maier et al. 2006; Nagao et al. 2006; Flores-Fajardo et al. 2011; Zhang et al. 2017; Sánchez 2020).

2022AandA...667A..90M__Priestley_&_Whitworth_2022_Instance_1

The line mass (mass per unit length) of G1.75-0.08 is Mline = 1011 ± 146 M⊙ pc−1. The dynamical state of the filament can be addressed by comparing its line mass with the virial or critical line mass (e.g. Fiege & Pudritz 2000, Eq. (12) therein). To calculate the latter quantity, we used the total (thermal+non-thermal) velocity dispersion, where the observed spectral line width (FWHM) was taken to be the FWHM of the HCN(1 – 0) line detected towards clump B in G1.75-0.08 from Miettinen (2014), that is, 13.50 ± 0.38 km s−1. We note that this is a very broad line width (and it is even broader (by a factor of 1.73) in clump A; Sect. 3.5), which could be attributed to multiple factors. From an observational point of view, the hyperfine structure of HCN was not resolved in the MALT90 spectra, which can lead to an overestimation of the line width although it was derived through fitting the hyperfine structure of the transition (Miettinen 2014). Moreover, the angular resolution of the Mopra telescope observations employed by Miettinen (2014) is 38″ (HPBW), which corresponds to 1.5 pc at the cloud distance, and hence the beam might have captured emission from the turbulent outer parts of the cloud. For example, if G1.75-0.08 follows the line width–size relation in the central molecular zone, or CMZ, which for HCN is found to be σ ∝ R0.62 (Shetty et al. 2012; Table 2 therein), the aforementioned HCN(1 – 0) line width would be expected to be only ~2.5 km s−1 on the ~0.1 pc scale, which is a typical inner width (FWHM) of filamentary molecular clouds (e.g. Arzoumanian et al. 2019; Priestley & Whitworth 2022, and references therein). We note that Henshaw et al. (2016a) derived a velocity dispersion of 11 km s−1 (~26 km s−1 FWHM for a Gaussian profile) for another Galactic centre region IRDC, namely G0.253+0.016 or the Brick from spectral line observations with Mopra, which is comparable to the observational results for G1.75-0.08 with the same telescope (Miettinen 2014). However, based on much higher angular resolution (1″.7) observations with the Atacama Large Millimetre/submillimetre Array (ALMA), Henshaw et al. (2019) derived an average velocity dispersion of 4.4 km s−1 in the Brick, which demonstrates that higher resolution spectral line observations towards G1.75-0.08 are also required. The effect of the angular resolution of the observations on the derived spectral line widths was also demonstrated by Hacar et al. (2018) in the case of the Orion integral filament (see e.g. Fig. 6 therein). From a physical point of view, there are several effects that can lead to line broadening. First, the HCN lines detected towards the G1.75-0.08 clumps were not optically thin, and hence the optical thickness effects might contribute to the broadening of the lines (e.g. Hacar et al. 2016). On the other hand, even the optically thin transitions detected towards the clumps had line widths that are comparable to those of HCN(1 – 0) (Miettinen 2014, Table 3 therein). Second, G1.75-0.08 might be associated with high-velocity gradients along the filament (e.g. Federrath et al. 2016; Gong et al. 2018) that would be blended in the MALT90 spectra and hence lead to overestimated line widths. Third, star formation driven outflows and shocks could lead to broad line profiles. Fourth, G1.75-0.08 is located close to the Galactic centre (RGC ≃ 270 pc), where the interstellar medium is highly turbulent (e.g. Salas et al. 2021 and references therein). At least part of this turbulent gas could contribute to our single-dish-observed spectral line widths. Obviously, further spectral line observations are needed to study the gas kinematics of G1.75-0.08 in more detail.

2022ApJ...937...97C__Bottrell_et_al._2019_Instance_1

Recently, machine learning (ML) has been applied to derive various physical parameters of galaxies (e.g., Masters et al. 2015; Krakowski et al. 2016; D’Isanto & Polsterer 2018; Bonjean et al. 2019; Davidzon et al. 2019; Hemmati et al. 2019; Chang et al. 2021) and improves on linear combinations through nonlinear activations (e.g., Ackermann et al. 2018; Walmsley et al. 2019; Ferreira et al. 2020; Bickley et al. 2021; Bickley et al. 2022; Ferreira et al. 2022). In particular, classification by ML (e.g., Banerji et al. 2010; Huertas-Company et al. 2015; Domínguez Sánchez et al. 2018; Bottrell et al. 2019; Pearson et al. 2019; Barchi et al. 2020; Chang et al. 2021) can avoid time-consuming visual inspections and will be helpful for the visual classification of galaxy−galaxy interactions from the forthcoming large surveys. For instance, Pearson et al. (2019) developed a convolutional neural network (CNN) architecture with observational SDSS and simulated EAGLE images to identify galaxy mergers. They showed that the networks achieve better performance in observational data than in simulations. Ferreira et al. (2020) achieve 0.90 accuracy to classify major mergers and measure galaxy mergers in all five CANDELS fields using CNN trained with simulated galaxies from the IllustrisTNG simulation and separate star-forming galaxies from post-mergers in a following work (Ferreira et al. 2022). Bickley et al. (2022) deployed a CNN and evaluated mock observations of simulated galaxies from the IllustrisTNG simulations to identify post-mergers. Bottrell et al. (2022) examine both the morphological and kinematic features of merger remnants from the TNG100 and show that the stellar kinematic data have few contributions. Moreover, it has been discussed whether ancillary information such as kinematics and spectroscopic information in addition to the images may provide an additional basis for classification (e.g., Nevin et al. 2019; Pan et al. 2019; Bottrell et al. 2022; McElroy et al. 2022). Therefore, it is important to identify specific features for classification with ML from both photometric and spectroscopic observables.

2018ApJ...864...31A__Smith_2007_Instance_1

The FORCAST images of HD 168625 are shown in Figure 4. The nebula is clearly resolved, with a partially complete ring structure that has two peaks almost symmetric around the star. We concur with Meixner et al. (1999) and O’Hara et al. (2003) in their interpretation of these two peaks as limb-brightened peaks of a torus of dust with a radius of ∼10″. The appearance is consistent with previously obtained images at 8.8, 12.5, and 20.6 μm from Meixner et al. (1999) and PACS 70 μm images (Groenewegen et al. 2011). We stress that our SOFIA/FORCAST images do not detect the outer polar rings seen in Spitzer IRAC images (Smith 2007), suggesting that the rings must be cold and below the sensitivity limits of SOFIA/FORCAST. The emission detected with the ring morphology in the IRAC band 4 image was probably polycyclic aromatic hydrocarbon (PAH) emission or atomic line emission, not thermal emission from warm dust. Figure 5 shows the temperature map that was derived from stacking the λFλ SOFIA/FORCAST 7.7–37.1 μm images and performing a least-squares fit of the dust temperature, Td, using the best-fit modified blackbody of the SED (i.e., Bλ(Td) · λ0.33) at each pixel location. The images were centered relative to one another by comparing the locations of the limb brightness peaks, and the 7.7–33.6 μm images were convolved with a 2D Gaussian kernel with an FWHM of 35 to match the resolution of the 34.8 and 37.1 μm images. Our temperature map shows a large gradient in dust temperatures with inner torus temperatures of ∼180 K and outer temperatures of ∼80 K, which is in agreement with the estimate of 170 ± 40 K obtained from our least-squares fit to the IR excess but is slightly higher than the equilibrium temperature estimates made by Pasquali et al. (2002; 113 K), Robberto & Herbst (1998; 135 K), and O’Hara et al. (2003; 130 K). The inaccuracies of this temperature map are due in large part to the fact that the method used to create it assumes that emission is purely thermal and that the dust shell is in thermal equilibrium. As noted previously, much of the 8.8–12.5 μm flux arises from transient, nonequilibrium emission from PAH grains. Therefore, using images at these wavelengths to derive quantitative conclusions from the temperature map has some limitations; however, we can interpret the maps qualitatively as discussed in Section 4.

2018AandA...620L...8H__Gundlach_&_Blum_2013_Instance_1

We adopted the model developed by Ďapek et al. (2005), in which 1D thermal conduction below each of the surface facets is solved numerically with the nonlinear Robin boundary condition at the surface, and the assumption of an isothermal core at a sufficient depth is made. A temperature-dependence of the thermal conductivity following Eq. (2) was used. For the sake of simplicity, the specific heat capacity c was assumed constant, c = 560 J kg−1 K−1, and the regolith grain density obtained for C-type meteorites was used, ρ = 3.11 g cm−3 (both from Gundlach & Blum 2013). We ran solutions for four values of the packing factor ϕ in the range between 0.3 and 0.6. Each time, the parameters of the thermal conductivity were adjusted to satisfy the constraints from thermal observations described in Sect. 3.3 (see Table 2). The time domain of one revolution about the Sun was divided into steps of 60 s, short enough when compared to the ≃3.6 h rotation period, and the space grid describing the depth below each of the surface increased exponentially, as described in Ďapek et al. (2005). We ensured that at each depth, the von Neumann stability condition was satisfied. Typically, ten iterative steps of the algorithm provide the temperature with an accuracy of one degree or better in the whole space and time domain of the solution. The shape and spin state of Phaethon was taken from the modeling in Sect. 3.1. Similarly, the volume-equivalent size of 5.1 km from Sect. 3.2 was used as an implicit value. The last parameter required to compute the thermal recoil acceleration (the Yarkovsky effect) is the bulk density of Phaethon. Our nominal models use 1 g cm−3 for the clarity, but we treated this value as a free parameter in the orbit determination process (similarly to what was done for asteroid Bennu in Chesley et al. 2014). Scaling to different densities is easily implemented by using the inverse-proportional dependence of the thermal acceleration on the bulk density. In our analysis we neglected the enhancement of the Yarkovsky effect that is due to surface roughness (Rozitis & Green 2012). This effect could cause an increase in our bulk density estimate of less than 10%, which is well within the formal uncertainty.

2022MNRAS.517.4202X__Lister_et_al._2018_Instance_1

Cavagnolo et al. (2010) suggested that the jet kinetic power is able to inflate the X-ray cavities or bubbles in different systems, including giant elliptical galaxies and cD galaxies (Type cD galaxy, a subtype of type-D giant elliptical galaxy), and proposed to evaluate the kinetic power Pkin = Pcav. However, this method is only limited to a small number of sources at present. It is known that the luminosity of extended region of radio jet, which is believed to be less Doppler-boosted, is related to jet kinetic power (Rawlings & Saunders 1991; Willott et al. 1999; Cavagnolo et al. 2010; Meyer et al. 2011), $P_{\rm rad} = \eta \, L_{\rm 5GHz}^{\rm ext}{}^{\kappa }$. Though the factor κ and η are given in discrepancy in literature due to the different sizes and source types of sample (Cavagnolo et al. 2010; Meyer et al. 2011), the ${\rm log}\, L^{\rm ext}_{\rm 5GHz}$ scales with the ${\rm log}\, P_{\rm rad}$ in the logarithmic space. We collect the total radio flux density from literature (Taylor et al. 1996 at 5 GHz; Piner & Edwards 2014 at 8.4 GHz; and Lister et al. 2018 at 15 GHz), and we convert the data at other frequencies to 5 GHz by assuming that (14)$$\begin{eqnarray*} S_{\rm 5GHz}^{\rm core} = S_{\rm \nu }^{\rm core} \, \, {\rm and} \, \, S_{\rm 5GHz}^{\rm ext} = S_{\rm \nu }^{\rm ext} \left(\frac{\nu }{\rm 5 \, GHz} \right)^{\alpha _{\rm ext}}, \end{eqnarray*}$$where the total radio flux is the sum of the flux of core and the flux of the extended region, Stot = Score + Sext, the αext = 0.75 and αcore = 0 (Fan et al. 2011; Pei et al. 2016, 2019, 2020). Together with the radio-core dominance parameter at 5 GHz (15)$$\begin{eqnarray*} R = \left(\frac{S^{\rm core}}{S^{\rm ext}} \right) (1+z)^{\alpha _{\rm core}-\alpha _{\rm ext}}, \end{eqnarray*}$$that we collect from Pei et al. (2020), we obtain $S_{\rm 5GHz}^{\rm ext}$ and calculate ${\rm log}\, L^{\rm ext}_{\rm 5GHz}$. The correlations of ${\rm log}\, P_{\rm rad}$ and ${\rm log}\, L_{\rm 5GHz}^{\rm ext}$ against ${\rm log}\, \beta _{\rm app}^{\rm max}$ are illustrated in Fig. 6 and linear regression results are listed in Table 2. Positive correlations of ${\rm log}\, P_{\rm rad}\, versus \, {\rm log}\, \beta _{\rm app}^{\rm max}$ and ${\rm log}\, L_{\rm 5GHz}^{\rm ext}\, versus \, {\rm log}\, \beta _{\rm app}^{\rm max}$ are found for blazars. The positive correlation of ${\rm log}\, P_{\rm rad}\, versus \, {\rm log}\, \beta _{\rm app}^{\rm max}$ holds for both FSRQs and BL Lacs when we consider them independently, while the positive correlation of ${\rm log}\, L_{\rm 5GHz}^{\rm ext}\, versus \, {\rm log}\, \beta _{\rm app}^{\rm max}$ only hold for BL Lacs. It is found that the motion of jet knots is significantly correlated with jet radiation power for both FSRQs and BL Lacs, however, the motion of jet knots is correlated with the kinetic power only for BL Lacs.

2015MNRAS.453.3414A__the_1999_Instance_2

Filippenko & Chornock (2001) first presented the dynamical estimate of mass of the source to be around 7.4 ± 1.1 M⊙. Recently, Radhika & Nandi (2014) claimed that the mass of XTE J1859+226 is perhaps in between 6.58 and 8.84 M⊙ which is similar to the prediction of Shaposhnikov & Titarchuk (2009), although the lower mass limit is estimated as 5.4 M⊙ by Corral-Santana et al. (2011). However, we consider the typical mass of the source as 7 M⊙. The distance of this source is around d ∼ 11 kpc (Filippenko & Chornock 2001). Steiner et al. (2013) measured the spin as ak ∼ 0.4; however, Motta et al. (2014b) recently reported that the spin of the source is ak ∼ 0.34. Since the spin predictions are quite close, we use ak ∼ 0.4 for this analysis. We estimate the fluxes Fx (see Table 1) of LHS and HIMS of the 1999 outburst of the source (Radhika & Nandi 2014). The corresponding disc luminosities are calculated as $L_{\rm disc}^{{\rm LHS}}=8.26 \times 10^{37}\ {\rm erg\ s^{-1}}$ and $L_{\rm disc}^{{\rm HIMS}}=1.85 \times 10^{38}\ {\rm erg\ s^{-1}}$, respectively. Now, it is reasonable to assume the accretion efficiency for rotating BH as η = 0.3 which corresponds to the accretion rate of the inflowing matter as ${\dot{M}}_{{\rm acc}}^{{\rm LHS}} = 0.304 {\dot{M}}_{{\rm Edd}}$ in LHS and ${\dot{M}}_{{\rm acc}}^{{\rm HIMS}} = 0.680 {\dot{M}}_{{\rm Edd}}$ in HIMS. For LHS, we use $R_{\dot{m}}=9.83$ per cent following our theoretical estimate where xs = 64.6rg for ak = 0.4, ${\mathcal {E}}=0.001\,98$ and λ = 3.18. Incorporating these inputs in equation (15), we obtain the jet kinetic power as $L^{{\rm LHS}}_{{\rm jet}} = 2.52\times 10^{37}\ {\rm erg\ s^{-1}}$. The maximum mass outflow rate for HIMS corresponding to ak = 0.4 is obtained from Fig. 9 as $R^{\rm max}_{\dot{m}}=17.5$ per cent for ${\mathcal {E}}=0.005\,47$ and λ = 3.1, where the shock transition occurs at 21.9rg. Using these values in equation (15), we obtain the maximum jet kinetic power as $L^{{\rm HIMS}}_{{\rm jet}} = 1.08\times 10^{38}\ {\rm erg\ s^{-1}}$ which we regard to be associated with the HIMS of this source.

2017MNRAS.472..940K__Malu_et_al._2010_Instance_1

Cluster mergers can stir the intra-cluster medium (ICM) and lead to complex distributions of density and temperatures. The X-ray surface brightness traces regions of high electron densities ($\propto n_e^{2}$) and the SZ is sensitive to the pressure (∝neT) along the line of sight. An offset in the peaks of these signals can be used as an indicator of the density and temperature distribution in the disturbed ICM. There are examples of merging clusters that show presence of X-ray–SZ offsets, such as Abell 2146 (AMI Consortium et al. 2011) and Bullet cluster (Malu et al. 2010). Simulations have shown that the offset is sensitive to initial relative velocity of the merging clusters and the mass ratio (e.g. Molnar, Hearn & Stadel 2012; Zhang, Yu & Lu 2014). The cluster PLCK G200.9−28.2  discussed in this work has its X-ray peak and the Planck detection peak offset by 3.4 arcmin, which is the extreme in the Planck  sample (PC12). The Planck  SZ positions have mean and median errors of 1.5 and 1.3 arcmin, respectively (Planck Collaboration IV 2013). The X-ray peak is located at the northern sub-cluster [Fig. 1(a) and Fig. 6(a)] and the Planck  SZ position is separated from it by 3.4 arcmin (700 kpc) in the direction of the radio relic. Due to the presence of a shock at the relic, the region is expected to be overpressured and thus may result in shifting the peak of the SZ-signal. The offset in the direction of the relic indicates possible physical origin for the offset in addition to the position reconstruction uncertainty of Planck. Based on the results of simulations, the offset can be explained as a result of two comparable mass sub-clusters with mass ratio between 1 and 3 (Zhang et al. 2014). Deep optical observations tracing the galaxy distribution in this cluster will be useful to measure the mass ratios of the sub-cluster masses. Due to the large uncertainty in the Planck  position we cannot analyse the offsets for a statistical sample of merging clusters. However, the offset in the X-ray and SZ positions opens an additional probe for understanding the properties of merging galaxy clusters.

2016ApJ...821..107G__Schwadron_et_al._2011_Instance_3

We repeated the plasma pressure calculation presented by Schwadron et al. (2011) and Fuselier et al. (2012) for the new ENA energy spectrum. The results for the downwind hemisphere and for the Voyager 1 region are summarized in Table 3. The measured intensity j ENA of neutralized hydrogen at a given energy translates into a pressure of the parent ion population in the heliosheath times the integration length along the line of sight, ΔP × l, in the following way: 3 Δ P × l = 4 π 3 n H m H v j ENA ( E ) σ ( E ) Δ E c f 4 c f = ( v + u R ) 2 v 4 ( v 2 + 4 u R 2 + 2 u R v ) . In Equation (3), ΔE denotes the width of the respective energy bin; for the typical radial velocity of solar wind in the flanks and the downwind hemisphere of the inner heliosheath, we assumed uR = 140 km s−1 as measured by Voyager 2, whereas uR = 40 km s−1 for the heliosheath in the Voyager 1 direction (Schwadron et al. 2011; Gloeckler & Fisk 2015). For the density of neutral hydrogen in the inner heliosheath a constant nH = 0.1 cm−3 is assumed (Schwadron et al. 2011; Gloeckler & Fisk 2015). The charge-exchange cross section between protons and neutral hydrogen decreases from (4 to 2) × 10−15 cm−2 for 0.015 to 1.821 keV (Lindsay & Stebbings 2005). The integration length l for ENA production in the plasma is approximately the thickness of the inner heliosheath. The part of Equation (3) without the velocity factor cf can be interpreted as stationary pressure. The total pressure or dynamic pressure is the stationary pressure times this factor. Integrating over all energy bins in Table 3, we obtain the total plasma pressure times heliosheath thickness as P × l = 304 pdyn cm−2 au for the downwind hemisphere and 66 pdyn cm−2 au for the Voyager 1 region (1 pdyn cm−2 au = 0.015 N m−1). If we want to put these numbers into the context of other studies, we face two problems. First, the uncertainty of the total pressure is large given the upper limits in the two lowest energy bins. Second, heliosheath plasma more energetic than 2 keV will produce ENAs that cannot be detected with IBEX-Lo. We therefore used the observed median j = 0 cm−2 sr−1 s−1 keV−1 for heliospheric ENAs in the two lowest energy bins of IBEX-Lo and relied on the study by Livadiotis et al. (2013). They compared the expected plasma pressure from a kappa distribution of protons with the plasma pressure derived from IBEX-Hi energy spectra: the energy range between 0.03 and 2 keV, roughly corresponding to the IBEX-Lo range, covered more than half of the total plasma pressure predicted from a kappa distribution. The authors found a total plasma pressure of P = 2.1 pdyn cm−2 for all sky directions except for the ENA Ribbon. Gloeckler & Fisk (2015) presented a multi-component plasma model for the heliosheath to explain Voyager and IBEX observations. At low energies they assumed the ENA energy spectra provided by Fuselier et al. (2012). They derived a total pressure of 2.5 pdyn cm−2 in all three plasma regions in the nose of the heliotail (Gloeckler & Fisk 2015). Pressure contributions from the slowed solar wind, magnetic pressure, and the pressure exerted from pickup ions and anomalous cosmic rays all had to be taken into account to obtain this total pressure.

2021MNRAS.500.5614A__Adrián-Martínez_et_al._2013_Instance_1

Using ANTARES data from the end of 2007–2017, a search for upward-going muon neutrinos and antineutrinos in spatial and temporal coincidence with 784 GRBs has been performed. The numerical model NeuCosmA was used to estimate the expected neutrino flux from each burst individually, in the context of one-zone internal shock model. A novel aspect of the search here presented is the inclusion in the data analysis chain of the uncertainty that possible unknown parameters, related to the characteristic activity of the central engine, can introduce in the neutrino flux evaluation. This is crucial in order to correctly interpret the validity of model-dependent results, in terms of upper limits set by non-detections of neutrinos in coincidence with GRBs (Adrián-Martínez et al. 2013; Aartsen et al. 2017). These parameters have been identified in the bulk Lorentz factor, variability time-scale, and source redshift, all of which are affecting the so-called dissipation radius, where shell collisions are realized. Among these parameters, the former was shown to impact the most GRB-neutrino flux predictions. At the same time, it is also possible to marginalize the uncertainty related to it by assuming a correlation with the source isotropic gamma-ray luminosity (which is in turn a physical observable). This was realized by relying upon the observational correlation found by Lü et al. (2012). As a result of such procedure, the minimum variability time-scale was found to contribute more than redshift to the uncertainty on the neutrino flux predictions from GRBs. Indeed, when letting tv free to vary, the estimated uncertainty on the neutrino flux expected from the model is observed to span up to several orders of magnitude. As a consequence, the expected ν-fluxes are provided with an uncertainty band of ±2σ. Analogously to previous ANTARES searches (Adrián-Martínez et al. 2013, 2017b; Celli et al. 2017), MC simulations of the signal predicted by NeuCosmA were performed, while the respective background was estimated directly from off-source data collected by ANTARES. Only track-like events reconstructed within 10° in radius from the expected GRB position were selected and in temporal correlation with the prompt gamma-ray emission.

2019MNRAS.487.5902K__Behroozi,_Wechsler_&_Conroy_2013_Instance_1

In the 50 Myr after a star particle is formed, it can undergo supernova (SN) explosions. These are randomly drawn from a delay-time distribution (Kimm et al. 2015). We employ the mechanical feedback model described in Kimm et al. (2015, 2017) and Rosdahl et al. (2018) and the amount of momentum injected into the gas depends on the phase of the SN that is resolved by the simulation as to capture the final momentum of the snowplow phase. The equivalent of 1051 ergs is injected into the gas for each SN. The maximum momentum that we inject is boosted according to Geen et al. (2015) to account for unresolved H ii regions (Kimm et al. 2017). For each massive star that explodes, 20 per cent of the mass is recycled back into the gas. This gas is metal enriched assuming a metallicity of 0.075. Following Rosdahl et al. (2018), we have calibrated the SN feedback in order to reproduce the high-redshift stellar mass–halo mass relation from abundance matching (Behroozi, Wechsler & Conroy 2013). For our simulation, this requires assuming that the mean SN progenitor mass is 5 M⊙ which leads to 4× more SN on average compared to a standard Kroupa IMF (Kroupa 2001). While not ideal, this results in galaxies that fall nicely on the stellar mass–halo mass relation (see fig. 1 of Katz et al. 2018) and produces a UV luminosity function consistent with observations for a similar set of simulations at approximately the same resolution (Rosdahl et al. 2018). While we cannot be certain that even our calibrated simulations have the correct stellar mass–halo mass relation as we are using a high-redshift extrapolation and there is intrinsically a lot of scatter, we have made an effort to calibrate on one of the best available predictions as any significant offset from the stellar mass–halo mass relation may lead to large systematic offsets in the SFR-line luminosity relations. In summary, our star formation and stellar feedback models are based on the work of Rosdahl et al. (2018) which were chosen to reproduce both a reasonable reionization history and UV luminosity function.

2017ApJ...850L..40A__Yang_et_al._2017_Instance_2

Aided by the tight localization constraints of the three-detector network and the proximity of the GW source, multiple independent surveys across the EM spectrum were launched in search of a counterpart beyond the sGRB (Abbott et al. 2017c). Such a counterpart, SSS17a (later IAU-designated AT 2017gfo), was first discovered in the optical less than 11 hours after merger, associated with the galaxy NGC 4993 (Coulter et al. 2017a, 2017b), a nearby early-type E/S0 galaxy (Lauberts 1982). Five other teams made independent detections of the same optical transient and host galaxy all within about one hour and reported their results within about five hours of one another (Allam et al. 2017; Arcavi et al. 2017a, 2017b; Lipunov 2017b; Tanvir & Levan 2017; Yang et al. 2017; Soares-Santos et al. 2017; Lipunov et al. 2017a). The same source was followed up and consistently localized at other wavelengths (e.g., Corsi et al. 2017; Deller et al. 2017a, 2017b, 2017c; Goldstein et al. 2017; Haggard et al. 2017a, 2017b; Mooley et al. 2017; Savchenko et al. 2017; Alexander et al. 2017; Haggard et al. 2017c; Goldstein et al. 2017; Savchenko et al. 2017). The source was reported to be offset from the center of the galaxy by a projected distance of about 10″ (e.g., Coulter et al. 2017a, 2017b; Haggard et al. 2017a, 2017b; Kasliwal et al. 2017; Yang et al. 2017; Yu et al. 2017). NGC 4993 has a Tully–Fisher distance of ∼40 Mpc (Freedman et al. 2001; NASA/IPAC Extragalactic Database164 164 The NASA/IPAC Extragalactic Database (NED) is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. ), which is consistent with the luminosity distance measurement from gravitational waves ( Mpc). Using the Tully–Fisher distance, the ∼10″ offset corresponds to a physical offset of ≃2.0 kpc. This value is consistent with offset measurements of sGRBs in other galaxies, though below the median value of ∼3–4 kpc (Fong et al. 2010; Fong & Berger 2013; Berger 2014).

2017ApJ...850...20G__Lonardoni_et_al._2015_Instance_1

The observation of massive neutron stars Demorest et al. (2010), Antoniadis et al. (2013) indicates that the EoS of nuclear matter must be very stiff in the regime of high densities and low temperatures. The degree of stiffness in the nuclear matter EoS is directly related to the repulsive interaction among particles at high densities, as well as to the particle content in the core of the stars. In particular, it has been extensively discussed in the literature whether exotic degrees of freedom might populate the core of neutron stars. On the one hand, it is more energetically favorable for the system to populate new degrees of freedom, such as hyperons (Dexheimer & Schramm 2008; Ishizuka et al. 2008; Bednarek et al. 2012; Fukukawa et al. 2015; Gomes et al. 2015; Maslov et al. 2015; Oertel et al. 2015; Lonardoni et al. 2015, 2016); Biswal et al. 2016; Burgio & Zappalà 2016; Chatterjee & Vidana 2016; Mishra et al. 2016; Vidaña 2016; Yamamoto et al. 2016; Tolos et al. 2017); Torres et al. 2017), delta isobars (Fong et al. 2010;Schurhoff et al. 2010; Drago et al. 2014; 2016; Cai et al. 2015; Zhu et al. 2016), and meson condensates (Ellis et al. 1995; Menezes et al. 2005; Takahashi 2007; Ohnishi et al. 2009; Alford et al. 2010; Fernandez et al. 2010; Mesquita et al. 2010; Mishra et al. 2010; Lim et al. 2014; Muto et al. 2015), in order to lower its Fermi energy (starting at about two times the saturation density). On the other hand, the EoS softening due to the appearance of exotica might turn some nuclear models incompatible with observational data, in particular with the recently measured massive neutron stars. One possible way to overcome this puzzle is the introduction of an extra repulsion in the YY interaction Schaffner & Mishustin (1996), Bombaci (2016), allowing models with hyperons to be able to reproduce massive stars (Dexheimer & Schramm 2008; Bednarek et al. 2012; Weissenborn et al. 2012; Banik et al. 2014; Bhowmick et al. 2014; Gusakov et al. 2014; Lopes & Menezes 2014; van Dalen et al. 2014; Yamamoto et al. 2014; Gomes et al. 2015). Another possible solution is the introduction of a deconfinement phase transition at high densities Bombaci (2016), with a stiff EoS for quark matter, usually associated with quark vector interactions (see Klähn et al. 2013 and references therein).

2019MNRAS.484.4083H__Kappenman_2006_Instance_1

The primary impact of the 1859 storm was on telegraphy (e.g. Boteler 2006). Today the principal ‘space weather’ threat is to the electric power grid (Baker et al. 2008; Hapgood, 2011, 2012; Oughton et al. 2016; Dyer et al. 2018). Because of this threat, several studies have been carried out to estimate how frequently such extreme space weather events may occur (e.g. Tsubouchi & Omura 2007; Riley 2012; Curto, Castell & Del Moral 2016; Riley & Love 2017). Such studies are dependent on the observations of a handful of storms that have approached or rivalled the Carrington geomagnetic storm in observed/inferred intensity and/or auroral extent. These include great storms in February 1872 (Chapman 1957a,b; Silverman 2008; Hayakawa et al. 2018c); September 1909 (Silverman 1995; Willis, Stephenson & Fang 2007; Love et al. 2019); May 1921 (Silverman & Cliver 2001; Kappenman 2006; Cliver & Dietrich 2013). The only such storm observed during the space era occurred in March 1989 (Allen et al. 1989; Silverman 2006a; Pulkkinen et al. 2012; Cid et al. 2014). More recently, in July 2012, a major backside eruption on the Sun was observed both remotely and in situ by the STEREO spacecraft (Kaiser et al. 2008; Russell et al. 2013; Liu et al. 2014; Riley et al. 2016). The interplanetary coronal mass ejection (CME) propagated to 1 AU in ∼20 h. Baker et al. (2013) calculated that had the eruption occurred on the frontside of the Sun – with optimal seasonal and local timing to maximize solar wind – magnetosphere coupling (worst case scenario (see e.g. Temerin & Li 2002) ─ it might have produced a storm greater than that inferred for the Carrington event. In addition to these storms, auroral evidence has recently been provided for two pre-1859 storms that may have ranked with the Carrington event: February 1730 (Hayakawa et al. 2018a), and, particularly, September 1770 (Willis et al. 1996; Nakazawa, Okada & Shiokawa 2004; Ebihara et al. 2017; Hayakawa et al. 2017e).

2020MNRAS.499.3792B__Pimbblet_2011_Instance_1

For consistency, we adopt the translation of this to the absolute velocities of cluster galaxies normalized by their respective galaxy cluster velocity dispersions into the range $0.3 \lt |\Delta \mathrm{V}|/\sigma _{r_{200}} \lt 0.5$ as deduced by Pimbblet (2011). Thus, if the mode of the standardised velocities for a sub-population has its foci at around $0.3 \lt |\Delta \mathrm{V}|/\sigma _{r_{200}} \lt 0.5$ for values around the virial radius, which we assume to be Rvirial ∼ r200, said sub-population would be classified as infalling. In contrast, a sub-population of backsplash cluster galaxies would be expected to peak significantly at $|\Delta \mathrm{V}|/\sigma _{r_{200}}\sim 0$ for values at or beyond our definition of the virial radius, with their fraction reaching zero at some upper limit (e.g. Mamon et al. 2004; Pimbblet 2011; Bahé et al. 2013; Haggar et al. 2020). Therefore, with respect to Fig. 5, we see that the column of our non-merging sub-populations across both bins of radius do not show any significant difference in the distributions of velocities with the exception of those that lie ≤r200, which show the non-AGN sub-population to occupy a mode within the range that nominally represents infallers, most likely for cluster galaxies 0.5 ≤ r200 1.0 (Gill et al. 2005). Additionally, the AGN sub-population slightly deviates from the non-AGN velocity distribution with a mode centred at $|\Delta \mathrm{\it V}|/\sigma _{r_{200}}\sim 0.8$, which could indicate stronger infalling. In contrast, the column of our merging AGN sub-populations shows the strongest deviations from the distribution of non-AGN, especially with the >r200 bin showing a significant centrally dominated AGN sub-population, where such a central dominance in relative velocity corresponds to a sub-population that were predominantly backsplash cluster galaxies. However, the dependence of this being the true nature of the sub-population relies upon more precise definitions of the radii since there is a natural upper limit a bound cluster galaxy can extend outward to with respect to its galaxy cluster’s potential, known as the splashback radius (More et al. 2015, 2016). In addition, Haggar et al. (2020) show that the fraction of backsplash galaxies diminishes by 2r200 and 2.5r200 for massive (∼×1015M⊙) merging and non-merging cluster systems, respectively, thus demonstrating that merging cluster environments experience a greater decrease in the fraction of harbouring backsplash galaxies as one continues to extend beyond r200. Indeed, the sub-populations of the merging cluster galaxies present in the ≤r200 bin show more variations in their general distributions with the modes of both the AGN and non-AGN sub-populations lying around $0.3 \lt |\Delta \mathrm{V}|/\sigma _{r_{200}} \lt 0.5$, which eludes to mostly infalling sub-populations rather than those associated with backsplash. Finally, if one considers the equivalent peak of the AGN density histogram at $\Delta \mathrm{V}|/\sigma _{r_{200}}\sim 1.7$ it could be possible there is a mix of recently accreted cluster galaxies and those that are relaxing on to a common potential. Although it should be noted that not much information can be confidently derived from the AGN sub-populations within the bins that possess small samples size (N ≲ 100), especially with the merging AGN-hosting cluster galaxies at ≤r200 that only has N = 15.