  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.
