Generative Adversarial Networks for the fast simulation of the Time Projection Chamber responses at the MPD detector
This work addresses the need for faster simulation models in high-energy physics experiments, offering a domain-specific solution that is incremental by building on existing GAN methods.
The authors tackled the problem of slow, resource-intensive detector simulations in high-energy physics by applying Generative Adversarial Networks (GANs) to simulate Time Projection Chamber responses at the MPD detector, achieving a speedup of more than an order of magnitude with no noticeable drop in reconstruction quality.
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.