Pythia Generated Jet Images for Location Aware Generative Adversarial Network Training

<p>Dataset containing 872666 jet images to train Location Aware Generative Adversarial Networks (LAGAN) for High Energy Physics. Results are published in [arXiv:1701.05927].</p> <p><strong>Format</strong>:<br> HDF5 file with the following fields:</p> <ul> <li>'image' : array of dim (872666, 25, 25), contains the pixel intensities of each 25x25 image</li> <li>'signal' : binary array to identify signal (1, i.e. W boson) vs background (0, i.e. QCD)</li> <li>'jet_eta': eta coordinate per jet</li> <li>'jet_phi': phi coordinate per jet</li> <li>'jet_mass': mass per jet</li> <li>'jet_pt': transverse momentum per jet</li> <li>'jet_delta_R': distance between leading and subleading subjets if 2 subjets present, else 0</li> <li>'tau_1', 'tau_2', 'tau_3': substructure variables per jet (a.k.a. n-subjettiness, where n=1, 2, 3)</li> <li>'tau_21': tau₂/tau₁ per jet</li> <li>'tau_32': tau₃/tau₂ per jet</li> </ul> <p><strong>Details</strong>:</p> <ul> <li>Simulated using Pythia 8.219 at √ s = 14 TeV</li> <li>Image pre-processing using method from in L. de Oliveira et al., Jet-Images -- Deep Learning Edition [arXiv:1511.05190]</li> <li>scikit-image==0.12.0 implementation of cubic spline rotation</li> <li>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]</li> <li>Jet clustering with anti-k_t algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k_t subjets</li> <li>Intensity of pixel = p_T of cell</li> <li>60 GeV &lt; m^jet &lt; 100 GeV</li> <li>250 GeV &lt; p_T^jet &lt; 300 GeV</li> <li>Sparse images (~10% NNZ)</li> </ul> <p>Full dataset description in [arXiv:1701.05927].</p>