Training and testing data for deep learning assisted jet tomography

When energetic partons traverse the quark gluon plasma (QGP), they will deposite energy and momentum into the medium. Mach cones are expected to form whose opening angles are tightly related to the speed of sound of QGP. This provides a way to detect the QGP equation of state. However, the mach cones are distorted by the collective expansion of QGP. The distortions depend on the initial jet production positions and its travelling direction. <br> We trained a deep point cloud neural network to locate the iniital jet production positions using the momenta of final state hadrons with transverse momentum pt&gt;2 GeV. This folder contains training and testing data for this AI4Science interdisplinary study. <br> There are 3 files in hdf5 format. 1. CoLBT_Hadrons_Frag.h5 (734.17 MB) , stores training and testing data from CoLBT model using fragmentation for particlization. <br> The data tables contained are listed below. gamma_pt_phi_eta_test Dataset {97908, 3} gamma_pt_phi_eta_train Dataset {78334, 3} hadrons_test Dataset {97908, 90, 6} hadrons_train Dataset {78334, 90, 6} ids_test Dataset {97908} ids_train Dataset {78334} jet_pt_phi_eta_test Dataset {97908, 3} jet_pt_phi_eta_train Dataset {78334, 3} jetxy_test Dataset {97908, 2} jetxy_train Dataset {78334, 2} where the data are split into training and testing sets. In the training set, gamma_pt_phi_eta_train is a 2D numpy array which stores the global information (pt, phi, pseudo-rapidity) of 78334 gamma triggers. hadrons_train is a numpy array of shape {78334, 90, 6} where 78334 is the number of events, 90 is the maximum number of hadrons in the jet cone and 6 is the number of features of each hadron. jet_pt_phi_eta_train is a numpy array of shape {78334, 3} where 3 stands for (pt, phi, eta) of the jet using jet finding algorithm. jetxy_train is a numpy array of shape {78334, 2} where 2 stands for (x, y). They are the jet production positions that the neural network is going to predict. <br> 2. CoLBT_Hadrons_Comb.h5 (408.4 MB), , stores training and testing data from CoLBT model using combination for particlization. <br> The data tables contained are listed below. gamma_pt_phi_eta_test Dataset {19615, 3} gamma_pt_phi_eta_train Dataset {78334, 3} hadrons_test Dataset {19615, 90, 6} hadrons_train Dataset {78334, 90, 6} ids_test Dataset {19615} ids_train Dataset {78334} jet_pt_phi_eta_test Dataset {19615, 3} jet_pt_phi_eta_train Dataset {78334, 3} jetxy_test Dataset {19615, 2} jetxy_train Dataset {78334, 2} where the data are split into training and testing sets. In the training set, gamma_pt_phi_eta_train is a 2D numpy array which stores the global information (pt, phi, pseudo-rapidity) of 78334 gamma triggers. hadrons_train is a numpy array of shape {78334, 90, 6} where 78334 is the number of events, 90 is the maximum number of hadrons in the jet cone and 6 is the number of features of each hadron. jet_pt_phi_eta_train is a numpy array of shape {78334, 3} where 3 stands for (pt, phi, eta) of the jet using jet finding algorithm. jetxy_train is a numpy array of shape {78334, 2} where 2 stands for (x, y). They are the jet production positions that the neural network is going to predict. <br> 3. Lido_Hadrons_Frag.h5 (186.22 MB), stores the testing data used for deep learning assisted jet tomography from LIDO Monte Carlo model which is different from CoLBT that is used for training. gamma_pt_phi_eta_test Dataset {44867, 3}, global information of gamma, (pt, phi, pseudo-rapidity) for 44867 events hadrons_test Dataset {44867, 90, 6}, final state hadrons for 44867 events, maximum number of hadrons is 90 for each event, number of features is 6 for each hadron. jet_pt_phi_eta_test Dataset {44867, 3}: the global information of the jet hadrons inside the cone. jetxy_test Dataset {44867, 2}: the production positions of the initial jet in the transverse plane.<br> <br>