Hybrid Machine-Learning Particle Identification for the ePIC Proximity-Focusing RICH
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917171340247040 |
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| author | Dongwi, D. H. Naïm, C. -J. Rhode, L. Deshpande, A. |
| author_facet | Dongwi, D. H. Naïm, C. -J. Rhode, L. Deshpande, A. |
| contents | We present a machine-learning-based particle-identification study for the proximity-focusing Ring Imaging Cherenkov (pfRICH) detector of the ePIC experiment at the Electron-Ion Collider. Operating in the backward region ($-3.5 \lesssim η\lesssim -1.5$), the pfRICH is designed to achieve at least $3σ$ separation among pions, kaons, and protons up to $7,\mathrm{GeV}/c$ for Semi-Inclusive Deep Inelastic Scattering measurements. Using a standalone Geant4 simulation of the pfRICH, we develop a hybrid machine-learning approach that combines convolutional neural-network-based feature extraction with gradient-boosted decision-tree classifiers. This method significantly enhances Cherenkov-ring pattern recognition and improves particle-separation performance, demonstrating the effectiveness of hybrid machine-learning techniques for next-generation Cherenkov detectors at the EIC. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_14598 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Machine-Learning Particle Identification for the ePIC Proximity-Focusing RICH Dongwi, D. H. Naïm, C. -J. Rhode, L. Deshpande, A. Instrumentation and Detectors High Energy Physics - Experiment We present a machine-learning-based particle-identification study for the proximity-focusing Ring Imaging Cherenkov (pfRICH) detector of the ePIC experiment at the Electron-Ion Collider. Operating in the backward region ($-3.5 \lesssim η\lesssim -1.5$), the pfRICH is designed to achieve at least $3σ$ separation among pions, kaons, and protons up to $7,\mathrm{GeV}/c$ for Semi-Inclusive Deep Inelastic Scattering measurements. Using a standalone Geant4 simulation of the pfRICH, we develop a hybrid machine-learning approach that combines convolutional neural-network-based feature extraction with gradient-boosted decision-tree classifiers. This method significantly enhances Cherenkov-ring pattern recognition and improves particle-separation performance, demonstrating the effectiveness of hybrid machine-learning techniques for next-generation Cherenkov detectors at the EIC. |
| title | Hybrid Machine-Learning Particle Identification for the ePIC Proximity-Focusing RICH |
| topic | Instrumentation and Detectors High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2512.14598 |