ML-based Fast Simulation of FARICH Responses
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866910235812167680 |
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| author | Shipilov, Foma Barnyakov, Alexander Ivanov, Artem Ratnikov, Fedor |
| author_facet | Shipilov, Foma Barnyakov, Alexander Ivanov, Artem Ratnikov, Fedor |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_17635 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ML-based Fast Simulation of FARICH Responses Shipilov, Foma Barnyakov, Alexander Ivanov, Artem Ratnikov, Fedor High Energy Physics - Experiment Machine Learning I.2.0 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. |
| title | ML-based Fast Simulation of FARICH Responses |
| topic | High Energy Physics - Experiment Machine Learning I.2.0 |
| url | https://arxiv.org/abs/2605.17635 |