ML-based Fast Simulation of FARICH Responses

• URL: http://arxiv.org/abs/2605.17635v2
• Date: Tue, 19 May 2026 14:46:34 GMT
• Title: ML-based Fast Simulation of FARICH Responses
• Abstract summary: We present a machine-learning-based approach to fast simulation of the FARICH detector response.<n>We propose a conditional Generative Adversarial Network (cGAN) with a lightweight convolutional architecture.<n>The cGAN produces realistic samples and provides a significant speed-up over Monte-Carlo simulation.
• Score: 39.49732181250791
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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