Generative Adversarial Networks for the fast simulation of the Time Projection Chamber responses at the MPD detector

Abstract

Detailed detector simulation models are vital for the successful operation of modern high-energy physics experiments. In most cases, running detailed models requires a significant amount of computing resources. It is desired to have approaches that are less resource-intensive. In this work, we demonstrate the applicability of Generative Adversarial Networks (GAN) as a basis for such fast-simulation models for the case of the Time Projection Chamber (TPC) at the MPD detector at the NICA accelerator complex. Our prototype GAN-based model of TPC works faster than the detailed simulation in an order of magnitude without any noticeable drop in the quality of the high-level reconstruction characteristics for the generated data. Approaches with direct and indirect quality metrics optimization are compared.