ML-based Fast Simulation of FARICH Responses

TL;DR

This paper introduces a machine learning approach using a conditional GAN to rapidly simulate FARICH detector responses in high-energy physics, significantly reducing computational costs.

Contribution

It presents a novel cGAN-based method for fast, realistic simulation of detector responses conditioned on particle parameters, outperforming traditional statistical baselines.

Findings

cGAN produces realistic detector response samples

Method offers significant speed-up over Monte-Carlo simulations

Comparison shows improved accuracy over linear baselines

Abstract

A fast simulation of the detector response is a vital task in high-energy physics (HEP). Traditional Monte-Carlo methods form the backbone of modern particle physics simulation software but are computationally expensive. We present a machine-learning-based approach to fast simulation of the Focusing Aerogel Ring Imaging Cherenkov (FARICH) detector response. Given a particle track and momentum, the goal is to generate realistic samples of photon hits on the detector matrix. We propose a conditional Generative Adversarial Network (cGAN) with a lightweight convolutional architecture that reproduces the projected detector response conditioned on particle parameters. We compare the cGAN against a linear statistical baseline using metrics applied to probability maps and to the reconstructed velocity distributions. The cGAN produces realistic samples and provides a significant speed-up over Monte-Carlo simulation.