ML-based Fast Simulation of FARICH Responses
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 agains...
| Authors | Foma Shipilov, Alexander Barnyakov, Artem Ivanov et al. |
|---|---|
| Primary category | hep-ex |
| Published | 17 May 2026 |
| Last updated at source | 19 May 2026 |
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