2025/12/16 by D. H. Dongwi, Dongwi, D. H., C. Naim +5
Physics and Astronomy · #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Instrumentation and Detectors (physics.ins-det) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies #Radiation Detection and Scintillator Technologies
paper · pdf · doi:10.48550/arxiv.2512.14598
openalex publication_date 2025/12/16 · openalex created_date 2025/12/18 · openalex updated_date 2026/07/28
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,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.