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≲η≲−1.5), the pfRICH is designed to achieve at least 3σ separation among pions, kaons, and protons up to 7,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.
@article{arxiv.2512.14598,
title = {Hybrid Machine-Learning Particle Identification for the ePIC Proximity-Focusing RICH},
author = {D. H. Dongwi and C. -J. Naïm and L. Rhode and A. Deshpande},
journal= {arXiv preprint arXiv:2512.14598},
year = {2025}
}