Pythia Generated Jet Images for Location Aware Generative Adversarial Network Training

Published: 7 February 2017| Version 1 | DOI: 10.17632/4r4v785rgx.1
Contributors:
Benjamin Nachman,
Luke de Oliveira,
Michela Paganini

Description

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 < m_jet < 100 GeV - 250 GeV < pT_jet < 300 GeV - Sparse images (~10% NNZ) Full dataset description in [arXiv:1701.05927].

Files

Steps to reproduce

Full code and instructions available at https://github.com/lukedeo/adversarial-jets/tree/master/generation. Tested on MacOS Sierra, Ubuntu16.04. Docker image available (from Docker Hub) under lukedeo/ji:latest Depends on Pythia, ROOT, FastJet and modern Python installation (only tested on 2.7, but should work on 3.4+).