Simulated particle-level dataset of ttbar with PU200 using Pythia8+Delphes3 for machine learned particle flow (MLPF)
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Dataset of 50,000 top quark-antiquark (ttbar) events produced in proton-proton collisions at 14 TeV, overlaid with minimum bias events corresponding to a pileup of 200 on average. The dataset consists of detector hits as the input, generator particles as the ground truth and reconstructed particles from DELPHES for additional validation.<br> The DELPHES model corresponds to a CMS-like detector with a multi-layered charged particle tracker, an electromagnetic and hadron calorimeter. Pythia8 and Delphes3 were used for the simulation. Each file contains a bzip2-compressed python pickle with the following contents: <pre><code class="language-python">> data = pickle.load(bz2.BZ2File("out/pythia8_ttbar/tev14_pythia8_ttbar_0_0.pkl.bz2", "rb")) # Each file contains lists of arrays X (detector elements), ygen (generator particles) and ycand (rule-based PF particles from Delphes) for 100 events > len(data["ycand"]), len(data["ygen"]), len(data["X"]) 100, 100, 100 #Each element in the list corresponds to an event. The first event in the file contains 5992 detector elements, ygen and ycand are 0-padded to the same length as X > data["X"][0].shape, data["ygen"][0].shape, data["ycand"][0].shape, ((5992, 12), (5992, 7), (5992, 7)) # The X rows are detector elements: calorimeter towers and tracks with the following 12-features (0-padded) # tower: [type==1, Et (GeV), eta, sin phi, cos phi, E (GeV), Eem (GeV), Ehad (GeV), 0, 0, 0, 0] # track: [type==2, pt (GeV), eta, sin phi, cos phi, P (GeV), eta_outer, sin phi_outer, cos phi_outer, charge, is_gen_muon, is_gen_electron] # The ygen (ycand) rows are generator-level truth particles (rule-based PF particles from Delphes) with the following features: # [pid, charge, pt (GeV), eta, sin phi, cos phi, E (GeV)] # pid==0: placeholder/padding entry # pid==1: charged hadrons # pid==2: neutral hadrons # pid==3: photons # pid==4: electrons # pid==5: muons</code></pre>
