February 7, 2017 Dataset Open Access

Pythia Generated Jet Images for Location Aware Generative Adversarial Network Training

Nachman, Benjamin; de Oliveira, Luke; Paganini, Michela

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-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].