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

arXiv: https://arxiv.org/abs/2203.16355 · HTML: https://mlsanity.com/paper/2203.16355/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 3 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 2 of 2)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks](https://mlsanity.com/paper/2012.04595/) (2020)

## Abstract (arXiv)

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.