Datasets:

jet-universe
/

jetclass2

+---
+license: mit
+tags:
+- physics
+pretty_name: JetClass-II
+size_categories:
+- 100B<n<1T
+---
+# 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 \\(p_\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)](https://home.cern/science/accelerators/large-hadron-collider) and beyond.
+## Dataset Details
+### Dataset Description
+<!-- Provide a longer summary of what this dataset is. -->
+The dataset consists of three major parts based on the jet origin and its substructure:
+1. **`Res2P`**: Generic \\(X\\) → 2 prong resonant jets.
+1. **`Res34P`**: Generic \\(X\\) → 3 or 4 prong resonant jets.
+1. **`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](https://cdn-uploads.huggingface.co/production/uploads/628ede0689851fc47c026d50/JXP-j8oksJpFx9KqjKTzM.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.parquet`—`Res2P_0199.parquet`   | **200** | **20M**   |
+| `Res2P`, val    | `Res2P_0200.parquet`—`Res2P_0249.parquet`   | 50  | 5M    |
+| `Res2P`, test   | `Res2P_0250.parquet`—`Res2P_0299.parquet`   | 50  | 5M    |
+| **`Res34P`, train** | `Res34P_0000.parquet`—`Res34P_0859.parquet` | **860** | **86M**   |
+| `Res34P`, val   | `Res34P_0860.parquet`—`Res34P_1074.parquet` | 215 | 21.5M |
+| `Res34P`, test  | `Res34P_1075.parquet`—`Res34P_1289.parquet` | 215 | 21.5M |
+| **`QCD`, train**    | `QCD_0000.parquet`—`QCD_0279.parquet`       | **280** | **28M**   |
+| `QCD`, val      | `QCD_0280.parquet`—`QCD_0349.parquet`       | 70  | 7M    |
+| `QCD`, test     | `QCD_0350.parquet`—`QCD_0419.parquet`       | 70  | 7M    |
+<!-- - **Curated by:** [More Information Needed]
+- **Funded by:** [More Information Needed] -->
+- **License:** MIT
+### Dataset Demo
+Use [[Colab]](https://colab.research.google.com/github/jet-universe/sophon/blob/main/notebooks/Interacting_with_JetClassII_and_Sophon.ipynb) 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](https://github.com/jet-universe/sophon).
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/628ede0689851fc47c026d50/jcXqSHqwSqO6ETIsqntIU.png)
+### Dataset Downloads
+To facilitate downloading, the HTTP links for all data files are provided in [`filelist.txt`](filelist.txt).
+### Dataset Sources
+- **Repository:** https://github.com/jet-universe/sophon
+- **Paper:** https://arxiv.org/abs/2405.12972
+- **Demo:** https://colab.research.google.com/github/jet-universe/sophon/blob/main/notebooks/Interacting_with_JetClassII_and_Sophon.ipynb
+## Uses
+<!-- Address questions around how the dataset is intended to be used. -->
+1. This dataset can be used to train models for a variety of jet-related tasks, such as jet classification, jet property regression, and jet generation or reconstruction.
+1. 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.
+1. 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).
+<!-- ### Direct Use -->
+<!-- This section describes suitable use cases for the dataset. -->
+<!-- ### Out-of-Scope Use -->
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+<!-- [More Information Needed] -->
+## 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 angular separation. The entry is left empty if no matched GEN-jet is found.
+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 \\(X\\) (in both 2-prong and 3/4-prong resonance cases).
+    2. The two secondary resonances \\(Y\\) produced by \\(X\\) ( \\(X \to Y_1Y_2\\) ) in the 3/4-prong resonance case.
+    3. The direct decay products (partons and leptons) from \\(X\\) and \\(Y\\).
+    4. The subsequent decay products of tau leptons in case (iii).
+    5. The partons ( \\(p_\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 \\(p_x\\) | ✔️
+| `part_py`              | vector\<float\>       | particle's \\(p_y\\) | ✔️
+| `part_pz`              | vector\<float\>       | particle's \\(p_z\\) | ✔️
+| `part_energy`          | vector\<float\>       | particle's energy | ✔️
+| `part_deta`            | vector\<float\>       | difference in pseudorapidity \\(\eta\\) between the particle and the jet axis | ✔️
+| `part_dphi`            | vector\<float\>       | difference in azimuthal angle \\(\phi\\) between the particle and the jet axis | ✔️
+| `part_d0val`           | vector\<float\>       | particle's transverse impact parameter value \\(d_0\\), in mm | ✔️
+| `part_d0err`           | vector\<float\>       | error of the particle's transverse impact parameter \\(\sigma_{d_0}\\), in mm | ✔️
+| `part_dzval`           | vector\<float\>       | particle's longitudinal impact parameter value \\(d_z\\), in mm | ✔️
+| `part_dzerr`           | vector\<float\>       | error of the particle's longitudinal impact parameter \\(\sigma_{d_z}\\), in mm | ✔️
+| `part_charge`          | vector\<int32_t\>     | particle's electric charge | ✔️
+| `part_isElectron`      | vector\<bool\>        | if the particle is an electron (`abs(pid)==11`) | ✔️
+| `part_isMuon`          | vector\<bool\>        | if the particle is an muon (`abs(pid)==13`) | ✔️
+| `part_isPhoton`        | vector\<bool\>        | if the particle is an photon (`pid==22`) | ✔️
+| `part_isChargedHadron` | vector\<bool\>        | if the particle is a charged hadron (`charge!=0 && !isElectron && !isMuon`) | ✔️
+| `part_isNeutralHadron` | vector\<bool\>        | if the particle is a neutral hadron (`charge==0 && !isPhoton`) | ✔️
+| **For jet** | | | |
+| `jet_pt`               | float                 | jet's transverse momentum \\(p_\mathrm{T}\\) | ✔️
+| `jet_eta`              | float                 | jet's pseudorapidity \\(\eta\\) | ✔️
+| `jet_phi`              | float                 | jet's azimuthal angle \\(\phi\\) | ✔️
+| `jet_energy`           | float                 | jet's energy | ✔️
+| `jet_sdmass`           | float                 | jet's soft-drop mass | ✔️
+| `jet_nparticles`       | int32_t               | number of jet constituent particles | ✔️
+| `jet_tau1`             | float                 | jet's \\(N\\)-subjettiness variable \\(\tau_1\\) | ✔️
+| `jet_tau2`             | float                 | jet's \\(N\\)-subjettiness variable \\(\tau_2\\) | ✔️
+| `jet_tau3`             | float                 | jet's \\(N\\)-subjettiness variable \\(\tau_3\\) | ✔️
+| `jet_tau4`             | float                 | jet's \\(N\\)-subjettiness variable \\(\tau_4\\) | ✔️
+| `jet_label`            | int32_t               | jet's label index in JetClass-II, detailed in the above table | 🆕
+| **For GEN-jet constituent particles** (if a GEN-jet is found matched to a jet) | | | |
+| `genpart_px`           | vector\<float\>       | particle's \\(p_x\\) | 🆕
+| `genpart_py`           | vector\<float\>       | particle's \\(p_y\\) | 🆕
+| `genpart_pz`           | vector\<float\>       | particle's \\(p_z\\) | 🆕
+| `genpart_energy`       | vector\<float\>       | particle's energy | 🆕
+| `genpart_jet_deta`     | vector\<float\>       | difference in pseudorapidity \\(\eta\\) between the particle and the jet (not the GEN-jet) axis | 🆕
+| `genpart_jet_dphi`     | vector\<float\>       | difference in azimuthal angle \\(\phi\\) between the particle and the jet (not the GEN-jet) axis | 🆕
+| `genpart_x`            | vector\<float\>       | \\(x\\) coordinate of the particle's production vertex, in mm | 🆕
+| `genpart_y`            | vector\<float\>       | \\(y\\) coordinate of the particle's production vertex, in mm | 🆕
+| `genpart_z`            | vector\<float\>       | \\(z\\) coordinate of the particle's production vertex, in mm | 🆕
+| `genpart_t`            | vector\<float\>       | \\(t\\) coordinate of the particle's production vertex, in mm/c | 🆕
+| `genpart_pid`          | vector\<int32_t\>     | particle's PDGID | 🆕
+| **For GEN-jet** (if matched to a jet) | | | |
+| `genjet_pt`            | float                 | GEN-jet's transverse momentum \\(p_\mathrm{T}\\) | 🆕
+| `genjet_eta`           | float                 | GEN-jet's pseudorapidity \\(\eta\\) | 🆕
+| `genjet_phi`           | float                 | GEN-jet's azimuthal angle \\(\phi\\) | 🆕
+| `genjet_energy`        | float                 | GEN-jet's energy | 🆕
+| `genjet_sdmass`        | float                 | GEN-jet's soft-drop mass | 🆕
+| `genjet_nparticles`    | int32_t               | number of GEN-jet constituent particles | 🆕
+| **For selected truth particles** | | | |
+| `aux_genpart_pt`       | vector\<float\>       | selected truth particles' \\(p_\mathrm{T}\\) | ✔️ (different rules to select truth particles)
+| `aux_genpart_eta`      | vector\<float\>       | selected truth particles' \\(\eta\\) | ✔️ (different rules to select truth particles)
+| `aux_genpart_phi`      | vector\<float\>       | selected truth particles' \\(\phi\\) | ✔️ (different rules to select truth particles)
+| `aux_genpart_mass`     | vector\<float\>       | selected truth particles' mass | ✔️ (different rules to select truth particles)
+| `aux_genpart_pid`      | vector\<int32_t\>     | selected truth particles' PDGID | 🆕
+| `aux_genpart_isResX`   | vector\<bool\>        | if the particle is the initial resonance \\(X\\) | 🆕
+| `aux_genpart_isResY`   | vector\<bool\>        | if the particle is the secondary resonance \\(Y\\) | 🆕
+| `aux_genpart_isResDecayProd` | vector\<bool\>  | if the particle is the direct decay product (parton and lepton) from \\(X\\) and \\(Y\\) | 🆕
+| `aux_genpart_isTauDecayProd` | vector\<bool\>  | if the particle is the subsequent decay product of tau leptons | 🆕
+| `aux_genpart_isQcdParton`    | vector\<bool\>  | if the particle is the parton with \\(p_\mathrm{T}\\) > 5 GeV stored in the QCD jet case | 🆕
+## 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`](https://github.com/jet-universe/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`](https://github.com/jet-universe/jetclass2_generation) repository to facilitate reproducibility.
+## Citation
+If you use the JetClass-II dataset, please cite:
+**BibTeX:**
+```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
+<!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. -->
+[More Information Needed]
+## Dataset Card Authors
+@jmgduarte, @colizz
+## Dataset Card Contact
+[More Information Needed]