Electron Identification Using Machine Learning in the MPD Experiment at NICA

Abstract We present studies of electron identification (eID) in the MPD experiment at NICA using machine learning techniques. The goal is to improve electron identification efficiency while preserving high purity, which is crucial for dielectron analyses. We compare electron identification performance between traditional cut-based approach and Machine learning. For machine learning based approach different classifiers, namely, Multi-Layer Perceptron (MLP) and Boosted Decision Tree (BDT) were trained with momentum-integrated and momentum-differential strategies using the CERN ROOT TMVA package.