GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response

High energy physics experiments essentially rely on simulated data for physics analyses. However, running detailed simulation models requires a tremendous amount of computation resources. New approaches to speed up detector simulation are therefore needed. The generation of calorimeter responses is often the most expensive component of the simulation chain for HEP experiments. It was shown that deep learning techniques, especially Generative Adversarial Networks, may be used to reproduce the calorimeter response. However, those applications are challenging, as the generated responses need evaluation not only in terms of image consistency: different physics-based quality metrics should be also taken into consideration. In our work, we develop a multitask GAN-based framework with the goal to speed up the response generation of the Electromagnetic Calorimeter (ECAL) of the LHCb detector at LHC. We introduce the Auxiliary Regressor as a second task to evaluate a proxy metric of the given input that is used by the Discriminator of the GAN. We show that this approach improves the stability of GAN and the model produces samples with better physics distributions.