Dataset containing 300k jet images that can be used to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics, such as the one in [arXiv:1701.05927]. Format: HDF5 file with the following fields: 'image' : array of dim (300000, 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==0.10.0 implementation of cubic spline rotation with fewer low energy artifacts than scikit-image>=0.12.0 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].

{"references": ["T. Sjostrand, S. Mrenna and P. Z. Skands, A Brief Introduction to PYTHIA 8.1, Comput. Phys. Commun. 178 (2008) 852\u2013867 [0710.3820].", "T. Sjostrand, S. Mrenna and P. Z. Skands, PYTHIA 6.4 Physics and Manual, JHEP 0605 (2006) 026 [hep-ph/0603175].", "L. de Oliveira, M. Kagan, L. Mackey, B. Nachman and A. Schwartzman, Jet-images \u2014 deep learning edition, JHEP 07 (2016) 069 [1511.05190]."]}

Generation and analysis code available at https://github.com/lukedeo/adversarial-jets