2023/03/19 by Nilay Kushawaha, Yulia Furletova, Kushawaha, Nilay +5
Physics and Astronomy · #Data Analysis #FOS: Physical sciences #High-Energy Particle Collisions Research #Instrumentation and Detectors (physics.ins-det) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies #Statistics and Probability (physics.data-an)
paper · pdf · doi:10.48550/arxiv.2303.10776
openalex publication_date 2023/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Machine learning (ML) is no new concept in the high-energy physics community, in fact, many ML techniques have been employed since the early 80s to deal with a broad spectrum of physics problems. In this paper, we present a novel technique to separate electrons from pions in the Gas Electron Multiplier Transition Radiation Detector (GEM TRD) using deep learning. The Artificial Neural Network (ANN) model is trained on the Monte Carlo data simulated using the ATHENA-based detector and simulation framework for the Electron-Ion Collider (EIC) experiment. The ANN model does a good job of separating electrons from pions.