A key aspect for the study of particle collisions is the comparison of the experiments data with those resulting from computer simulations, mainly obtained using Monte Carlo-based generators. However the amount of data required in simulations makes this task very time consuming. One approach to avoid this issue is by using machine learning techniques to speed up this process.
In this work, we focus on the simulation of one of the final-state objects of particle collisions that are the hadronic jets. For this study the input dataset consists of the particle constituents of the jets due to its sparsity and the possibility to assay the network's capacity to describe the jets and particles properties. The generative neural network architecture chosen is a variational autoencoder consisting of convolutional layers. For the reconstruction error term we choose a permutation-invariant loss on the particles' properties along with mean-squared error terms measuring the distinction between input and output jets transverse momentum and mass, which imposes physics constraints, allowing the model to learn the kinematics of the jets.
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