Separation of electrons from pions in GEM TRD using deep learning

TL;DR

This paper introduces a deep learning approach using neural networks to improve the separation of electrons from pions in GEM TRD detectors, enhancing particle identification in high-energy physics experiments.

Contribution

The study presents a novel application of deep learning with neural networks for particle discrimination in GEM TRD, trained on simulated EIC data.

Findings

ANN model effectively separates electrons from pions

Deep learning improves particle identification accuracy

Model trained on ATHENA-based Monte Carlo data

Abstract

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.