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Dataset Card for JetClass-II

JetClass-II is a large-scale and comprehensive dataset covering extensive large-radius jet signatures and a wide range of jet pTp_\mathrm{T} and mass values.

The dataset is designed to develop large and comprehensive jet models, intended for various applications, to support extensive physics searches and measurements at the Large Hadron Collider (LHC) and beyond.

Dataset Details

Dataset Description

The dataset consists of three major parts based on the jet origin and its substructure:

  1. Res2P: Generic XX β†’ 2 prong resonant jets.
  2. Res34P: Generic XX β†’ 3 or 4 prong resonant jets.
  3. QCD: Jets from QCD multijet background.

Each part is further subdivided into detailed categories, indicating which partons, leptons, or combinations thereof initiated the jet.

image/png

The three major parts (Res2P, Res34P, and QCD) are separately packed and can be downloaded individually for ease of use. The sizes of the training sets are 20M, 86M, and 28M entries, respectively. The dataset also includes validation and test sets, with the sizes for training/validation/test following a 4:1:1 ratio.

Every 100k entries (jets) are stored in a Parquet file. A complete view of the JetClass-II data files is shown in the table below.

Type File name range File number total entries
Res2P, train Res2P_0000.parquetβ€”Res2P_0199.parquet 200 20M
Res2P, val Res2P_0200.parquetβ€”Res2P_0249.parquet 50 5M
Res2P, test Res2P_0250.parquetβ€”Res2P_0299.parquet 50 5M
Res34P, train Res34P_0000.parquetβ€”Res34P_0859.parquet 860 86M
Res34P, val Res34P_0860.parquetβ€”Res34P_1074.parquet 215 21.5M
Res34P, test Res34P_1075.parquetβ€”Res34P_1289.parquet 215 21.5M
QCD, train QCD_0000.parquetβ€”QCD_0279.parquet 280 28M
QCD, val QCD_0280.parquetβ€”QCD_0349.parquet 70 7M
QCD, test QCD_0350.parquetβ€”QCD_0419.parquet 70 7M
  • License: MIT

Dataset Demo

Use [Colab] to inspect and visualize data in JetClass-II.

This demo will showcase visualizations of jets, annotated with the top 5 probability scores as interpreted by the Sophon model, the first application on JetClass-II.

image/png

Dataset Downloads

To facilitate downloading, the HTTP links for all data files are provided in filelist.txt.

Dataset Sources

Uses

  1. This dataset can be used to train models for various jet-related tasks, such as jet classification, jet property regression, and jet generation or reconstruction.
  2. The dataset's extensive phase space coverage and high statistics enable model developers to focus on specific regions of interest, or work with the entire dataset, enabling the creation of specialized models for particular phase spaces or pre-training a more general model.
  3. The dataset contains detailed low-level information to support customized model training strategies, including kinematic features, particle IDs, and trajectory displacement information for both jet constituent particles and relevant generator-level particles (see details in the next section).

Dataset Structure

The JetClass-II dataset includes the following variables:

  1. part_*: Features for jet constituent particles (i.e., E-flow objects in Delphes).
  2. jet_*: Features for jets. A specific variable is jet_label, which indicates the label in 188 classes.
  3. genpart_*: Features for generator-level jet (GEN-jet) constituent particles. The GEN-jet is clustered from the stable particles generated by Pythia, excluding neutrinos, using the same clustering configuration. The GEN-jets are matched with jets based on the pseudoangular separation Ξ”R\Delta R. Jets, ordered by decreasing pTp_\mathrm{T}, are paired with the closest unmatched GEN-jet. If no matched GEN-jet is found, the entry is left empty, which occurs in only 0.2–0.8% of cases.
  4. genjet_*: Jet-level features for the matched GEN-jet.
  5. aux_genpart_*: Auxiliary variables storing features of selected truth particles. Five types of particles are chosen if they are valid:
    1. The initial resonance XX (in both 2-prong and 3/4-prong resonance cases).
    2. The two secondary resonances YY produced by XX ( X→Y1Y2X \to Y_1Y_2 ) in the 3/4-prong resonance case.
    3. The direct decay products (partons and leptons) from XX and YY.
    4. The subsequent decay products of tau leptons in case (iii).
    5. The partons ( pTp_\mathrm{T} > 5 GeV) matched within a QCD jet.
Variable Type Description Exists in JetClass?
For jet constituent particles
part_px vector<float> particle's pxp_x βœ”οΈ
part_py vector<float> particle's pyp_y βœ”οΈ
part_pz vector<float> particle's pzp_z βœ”οΈ
part_energy vector<float> particle's energy βœ”οΈ
part_deta vector<float> difference in pseudorapidity Ξ·\eta between the particle and the jet axis βœ”οΈ
part_dphi vector<float> difference in azimuthal angle Ο•\phi between the particle and the jet axis βœ”οΈ
part_d0val vector<float> particle's transverse impact parameter value d0d_0, in mm βœ”οΈ
part_d0err vector<float> error of the particle's transverse impact parameter Οƒd0\sigma_{d_0}, in mm βœ”οΈ
part_dzval vector<float> particle's longitudinal impact parameter value dzd_z, in mm βœ”οΈ
part_dzerr vector<float> error of the particle's longitudinal impact parameter Οƒdz\sigma_{d_z}, in mm βœ”οΈ
part_charge vector<int32_t> particle's electric charge βœ”οΈ
part_isElectron vector<bool> if the particle is an electron (abs(pid)==11) βœ”οΈ
part_isMuon vector<bool> if the particle is an muon (abs(pid)==13) βœ”οΈ
part_isPhoton vector<bool> if the particle is an photon (pid==22) βœ”οΈ
part_isChargedHadron vector<bool> if the particle is a charged hadron (charge!=0 && !isElectron && !isMuon) βœ”οΈ
part_isNeutralHadron vector<bool> if the particle is a neutral hadron (charge==0 && !isPhoton) βœ”οΈ
For jets
jet_pt float jet's transverse momentum pTp_\mathrm{T} βœ”οΈ
jet_eta float jet's pseudorapidity Ξ·\eta βœ”οΈ
jet_phi float jet's azimuthal angle Ο•\phi βœ”οΈ
jet_energy float jet's energy βœ”οΈ
jet_sdmass float jet's soft-drop mass βœ”οΈ
jet_nparticles int32_t number of jet constituent particles βœ”οΈ
jet_tau1 float jet's NN-subjettiness variable Ο„1\tau_1 βœ”οΈ
jet_tau2 float jet's NN-subjettiness variable Ο„2\tau_2 βœ”οΈ
jet_tau3 float jet's NN-subjettiness variable Ο„3\tau_3 βœ”οΈ
jet_tau4 float jet's NN-subjettiness variable Ο„4\tau_4 βœ”οΈ
jet_label int32_t jet's label index in JetClass-II, detailed in the above table πŸ†•
For GEN-jet constituent particles (if a GEN-jet is found matched to a jet)
genpart_px vector<float> particle's pxp_x πŸ†•
genpart_py vector<float> particle's pyp_y πŸ†•
genpart_pz vector<float> particle's pzp_z πŸ†•
genpart_energy vector<float> particle's energy πŸ†•
genpart_jet_deta vector<float> difference in pseudorapidity Ξ·\eta between the particle and the jet (not the GEN-jet) axis πŸ†•
genpart_jet_dphi vector<float> difference in azimuthal angle Ο•\phi between the particle and the jet (not the GEN-jet) axis πŸ†•
genpart_x vector<float> xx coordinate of the particle's production vertex, in mm πŸ†•
genpart_y vector<float> yy coordinate of the particle's production vertex, in mm πŸ†•
genpart_z vector<float> zz coordinate of the particle's production vertex, in mm πŸ†•
genpart_t vector<float> tt coordinate of the particle's production vertex, in mm/c πŸ†•
genpart_pid vector<int32_t> particle's PDGID πŸ†•
For GEN-jets (if matched to a jet)
genjet_pt float GEN-jet's transverse momentum pTp_\mathrm{T} πŸ†•
genjet_eta float GEN-jet's pseudorapidity Ξ·\eta πŸ†•
genjet_phi float GEN-jet's azimuthal angle Ο•\phi πŸ†•
genjet_energy float GEN-jet's energy πŸ†•
genjet_sdmass float GEN-jet's soft-drop mass πŸ†•
genjet_nparticles int32_t number of GEN-jet constituent particles πŸ†•
For selected truth particles
aux_genpart_pt vector<float> selected truth particles' pTp_\mathrm{T} βœ”οΈ (different rules to select truth particles)
aux_genpart_eta vector<float> selected truth particles' Ξ·\eta βœ”οΈ (different rules to select truth particles)
aux_genpart_phi vector<float> selected truth particles' Ο•\phi βœ”οΈ (different rules to select truth particles)
aux_genpart_mass vector<float> selected truth particles' mass βœ”οΈ (different rules to select truth particles)
aux_genpart_pid vector<int32_t> selected truth particles' PDGID πŸ†•
aux_genpart_isResX vector<bool> if the particle is the initial resonance XX πŸ†•
aux_genpart_isResY vector<bool> if the particle is the secondary resonance YY πŸ†•
aux_genpart_isResDecayProd vector<bool> if the particle is the direct decay product (parton and lepton) from XX and YY πŸ†•
aux_genpart_isTauDecayProd vector<bool> if the particle is the subsequent decay product of tau leptons πŸ†•
aux_genpart_isQcdParton vector<bool> if the particle is the parton with pTp_\mathrm{T} > 5 GeV stored in the QCD jet case πŸ†•

Dataset Creation

The dataset is generated using MadGraph + Pythia + Delphes.

During the Delphes (fast simulation) step, the pileup (PU) effect, with an average of 50 PU interactions, is emulated to mimic the realistic LHC collision environment. The PUPPI algorithm is then applied to remove the PU, correcting the E-flow objects used to cluster jets. This distinguishes it from the original JetClass dataset. The Delphes card can be found in the jetclass2-generation repository.

The complete generation script (the one-stop MadGraph + Pythia + Delphes production) and the n-tuplizer script are provided in the jetclass2-generation repository to facilitate reproducibility.

Citation

If you use the JetClass-II dataset, please cite:

BibTeX:

@article{Li:2024htp,
    author = "Li, Congqiao and Agapitos, Antonios and Drews, Jovin and Duarte, Javier and Fu, Dawei and Gao, Leyun and Kansal, Raghav and Kasieczka, Gregor and Moureaux, Louis and Qu, Huilin and Suarez, Cristina Mantilla and Li, Qiang",
    title = "{Accelerating Resonance Searches via Signature-Oriented Pre-training}",
    eprint = "2405.12972",
    archivePrefix = "arXiv",
    primaryClass = "hep-ph",
    month = "5",
    year = "2024"
}

Glossary

A good resource is the CERN Open Data Portal Glossary: https://opendata.cern.ch/search?q=&f=type%3AGlossary&l=list&order=asc&p=1&s=10&sort=title

Jet: A jet is a shower of hadrons, which originate from quarks or gluons, clustered together after being produced in particle collisions. A large-radius jet is clustered using a larger radius parameter RR (0.8 in this dataset) and may result from a collection of nearby quarks, gluons, and other particles.

Constituent particle: The particles (reconstructed hadrons, electrons, muons, or photons) that form the jet after clustering.

GEN-jet: A generator-level jet, reconstructed from a list of stable truth particles in a simulation.

Truth particle: Particles produced during a collision in a simulation. Initial truth particles are directly generated in the hard collision process, but they may undergo decays, intermediate emissions, and parton showering to produce the stable particles ultimately observed by the detector. These stable particles are used to cluster GEN-jets.

Pseudorapidity Ξ·\eta: The pseudorapidity Ξ·\eta is a coordinate that describes the angle of a particle (or jet) produced in an event relative to the beam axis. It is calculated as Ξ·=βˆ’ln⁑(tan⁑θ2)\eta = - \ln \left ( \tan \frac{\theta}{2} \right ), with ΞΈ\theta the angle between the three-momentum and the beam axis. Ξ·=0\eta=0 means the produced particle/jet is perpendicular to the beam axis, while a higher pseudorapidity means it is closer to the beam.

Transverse momentum pTp_\mathrm{T}: The component of the momentum of a particle (or jet) that is transverse (i.e., perpendicular) to the beam axis.

Pseudoangular distance Ξ”R\Delta R: Ξ”R(a, b)=(Ξ·aβˆ’Ξ·b)2+(Ο•aβˆ’Ο•b)2\Delta R(a,\,b) = \sqrt{(\eta_a - \eta_b)^2 + (\phi_a - \phi_b)^2}, where Ξ·β€…β€Š(Ο•)\eta\;(\phi) is the pseudorapidity (azimuthal angle) of the momentum of a particle or a jet. A particle is considered matched to the jet if Ξ”R(particle,β€…β€Šjet axis)>R0\Delta R\mathrm{(particle,\;jet\,axis)} > R_0, where R0R_0 is the jet radius parameter.

Impact parameter: The distance of the closest approach of the track to the collision point.

Particle's PDGID: A unique identifier assigned to each particle type by the Particle Data Group (PDG). The full PDGID table can be accessed here.

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Dataset Card Authors and Contacts

@jmgduarte, @colizz

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