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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 witha broad spectrum of physics problems. In this paper, we present a noveltechnique to separate electrons from pions in the Gas Electron MultiplierTransition Radiation Detector (GEM TRD) using deep learning. The ArtificialNeural Network (ANN) model is trained on the Monte Carlo data simulated usingthe ATHENA-based detector and simulation framework for the Electron-IonCollider (EIC) experiment. The ANN model does a good job of separatingelectrons from pions.

Separation of electrons from pions in GEM TRD using deep learning