Electromagnetic shower generation with Graph Neural Networks

In this work, we propose an approach for electromagnetic shower generation on a track level. Currently, Monte Carlo simulation occupies 50-70% of total computing resources that are used by physicists experiments worldwide. Thus, speedup of the simulation step allows to reduce simulation cost and accelerate synthetic experiments. In this paper, we suggest dividing the problem of shower generation into two separate issues: graph generation and tracks features generation. Both these problems can be efficiently solved with a cascade of deep autoregressive generative network and graph convolution network. The novelty of the proposed approach lies in the application of graph neural networks to the generation of a complex recursive physical process.