Pythia Generated Jet Images for Location Aware Generative Adversarial Network Training

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].