Pythia Generated Jet Images with Alternative Rotation Scheme for Location Aware Generative Adversarial Network Training

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-k_t algorithm with a radius R = 1.0 using FastJet 3.2.1; constituent re-clustering into R = 0.3 k_t subjets
• Intensity of pixel = p_T of cell
• 60 GeV < m^jet < 100 GeV
• 250 GeV < p_T^jet < 300 GeV
• Sparse images (~10% NNZ)

Full dataset description in [arXiv:1701.05927].