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Dataset Open Access

Pythia8 Quark and Gluon Jets for Energy Flow

Komiske, Patrick; Metodiev, Eric; Thaler, Jesse

A dataset of quark and gluon jets generated with Pythia 8, originally used in Energy Flow Networks: Deep Sets for Particle Jets and available through the EnergyFlow Python package. Generation parameters are listed below:

  • Pythia 8.226, \(\sqrt{s}=14\,\text{TeV} \)
  • Quarks (uds only) from WeakBosonAndParton:qg2gmZq, gluons from WeakBosonAndParton:qqbar2gmZg with the Z decaying to neutrinos
  • FastJet 3.3.0, anti-ki jets with R=0.4
  • \(p_T^\text{jet}\in[500,550]\,\text{GeV},\,|y^\text{jet} |<1.7\)

Each file is in compressed NumPy format and can be accessed in python using np.load. There are two arrays in each file

  • X: (100000,M,4), exactly 50k quark and 50k gluon jets, randomly sorted, where M is the max multiplicity of the jets in that file (other jets have been padded with zero-particles), and the features of each particle are its pt, rapidity, azimuthal angle, and pdgid.
  • y: (100000,), an array of labels for the jets where gluon is 0 and quark is 1.

If you use this dataset, please cite this Zenodo record as well as the corresponding paper:

  • P. T. Komiske, E. M. Metodiev, J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121, arXiv:1810.05165.

The data format is additionally described as part of the EnergyFlow documentation. For the corresponding dataset of Herwig jets, see this Zenodo record.

Files (2.1 GB)
Name Size
QG_jets.npz
md5:f5d052f10a79c6e8b9382637aca0ef52
106.7 MB Download
QG_jets_1.npz
md5:f6a1081c76a47386bc11abcf0e499552
106.5 MB Download
QG_jets_10.npz
md5:4b424b553e1e7f852e47ea9904bc2dcf
106.5 MB Download
QG_jets_11.npz
md5:ccd29c9d1abb34dd7cfb48cfc57a9695
106.6 MB Download
QG_jets_12.npz
md5:1ed1f6f19fb8439c9811dced41d5127d
106.7 MB Download
QG_jets_13.npz
md5:af45818c361e11ca9b3adaba30db06ad
106.8 MB Download
QG_jets_14.npz
md5:488ced3ea409d7e2b196da67f7d182ec
106.6 MB Download
QG_jets_15.npz
md5:c5e083019de6cd6a0ef12bcec1ea566b
106.7 MB Download
QG_jets_16.npz
md5:48605d55edff665f0c7d2f800b5a622e
106.7 MB Download
QG_jets_17.npz
md5:8fd47760957b5fd9adec9048b50cd1a9
106.5 MB Download
QG_jets_18.npz
md5:d43d611484b55391e891ba31c605f792
106.6 MB Download
QG_jets_19.npz
md5:6753508e34014cc69714a01fca20ec38
106.8 MB Download
QG_jets_2.npz
md5:2628367c57ba598f4473c870d1381041
106.5 MB Download
QG_jets_3.npz
md5:dd3ad998b0a1bd9acea2ecf029a8a921
106.4 MB Download
QG_jets_4.npz
md5:a56d6bb98361b55382aa8c06225e05d8
106.6 MB Download
QG_jets_5.npz
md5:266c688e9e6ff1cd20840692d45eaaf8
106.6 MB Download
QG_jets_6.npz
md5:95a9f7e555fb7b1073967056b9030b11
106.7 MB Download
QG_jets_7.npz
md5:4ae72aaabe121bd489532c99a6bdde95
106.7 MB Download
QG_jets_8.npz
md5:a2b80bd4199468fde4f302d346a8c9d8
106.8 MB Download
QG_jets_9.npz
md5:1157cbace488c70c9dcfc250f3345b06
106.6 MB Download
  • P. T. Komiske, E. M. Metodiev, EnergyFlow, https://energyflow.network.

  • P. T. Komiske, E. M. Metodiev, J. Thaler, Energy Flow Networks: Deep Sets for Particle Jets, JHEP 01 (2019) 121, arXiv:1810.05165.

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