Dataset Open Access

Pythia Generated Jet Images for Location Aware Generative Adversarial Network Training

Nachman, Benjamin; de Oliveira, Luke; Paganini, Michela

Dataset containing 872666 jet images to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics. Results are published in [arXiv:1701.05927].

Format:
HDF5 file with the following fields:

  • 'image' : array of dim (872666, 25, 25), contains the pixel intensities of each 25x25 image
  • 'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)
  • 'jet_eta': eta coordinate per jet
  • 'jet_phi': phi coordinate per jet
  • 'jet_mass': mass per jet
  • 'jet_pt': transverse momentum per jet
  • 'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0
  • 'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)
  • 'tau_21': tau2/tau1 per jet
  • 'tau_32': tau3/tau2 per jet

Details:

  • Simulated using Pythia 8.219 at √ s = 14 TeV
  • Image pre-processing using method from in L. de Oliveira et al., Jet-Images -- Deep Learning Edition [arXiv:1511.05190]
  • scikit-image==0.12.0 implementation of cubic spline rotation
  • Finite calorimeter granularity simulated with 0.1×0.1 grid in η and φ, with η × φ ∈ [−1.25, 1.25] × [−1.25, 1.25]
  • Jet clustering with anti-kt algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 kt subjets
  • Intensity of pixel = pT of cell
  • 60 GeV < mjet < 100 GeV
  • 250 GeV < pTjet < 300 GeV
  • Sparse images (~10% NNZ)

Full dataset description in [arXiv:1701.05927].

Generation and analysis code available at https://github.com/lukedeo/adversarial-jets To reproduce this dataset: - follow instructions available at https://github.com/lukedeo/adversarial-jets/tree/master/generation - docker image available (from Docker Hub) under lukedeo/ji:latest - tested on MacOS Sierra, Ubuntu16.04 - depends on Pythia, ROOT, FastJet and modern Python installation (only tested on 2.7, but should work on 3.4+)
Files (2.2 GB)
Name Size
jet-images_Mass60-100_pT250-300_R1.25_Pix25.hdf5
md5:f9b11c46b6a0ff928bec2eccf865ecf0
2.2 GB Download
  • L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman, Jet-images — deep learning edition, JHEP 07 (2016) 069 [1511.05190].

  • T. Sjostrand, S. Mrenna and P. Z. Skands, A Brief Introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852–867 [0710.3820].

  • T. Sjostrand, S. Mrenna and P. Z. Skands, PYTHIA 6.4 Physics and Manual, JHEP 0605 (2006) 026 [hep-ph/0603175].

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