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

TL;DR

This paper presents a GAN-based fast simulation model for the Time Projection Chamber at the MPD detector, achieving over ten times faster performance with comparable quality to detailed simulations.

Contribution

It introduces a novel GAN approach for TPC simulation that significantly reduces computational resources needed while maintaining high-quality data generation.

Findings

GAN model is over ten times faster than detailed simulation

No noticeable quality loss in high-level reconstruction characteristics

Comparison of direct and indirect quality metrics optimization methods

Abstract

The detailed detector simulation models are vital for the successful operation of modern high-energy physics experiments. In most cases, such detailed models require a significant amount of computing resources to run. Often this may not be afforded and less resource-intensive approaches are desired. In this work, we demonstrate the applicability of Generative Adversarial Networks (GAN) as the 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 more than an order of magnitude faster compared to the detailed simulation 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.