Validating a BDT-based Electron-Positron Identification Algorithm at CLAS12 with Experimental Data

Abstract

This article presents a machine-learning, particle-identification algorithm for electrons and positrons in the CLAS12 experiment. The main objective was to minimize charged-pion contamination for both experimental and simulated data. We developed, evaluated and validated two BDT models with different input features, both trained on simulated samples, and conducted rigorous validation using both simulated and experimental data to ensure reliability. Our results show the effectiveness of the applied models in mitigating background contamination. By retaining more than 90\% of leptons in the simulated samples, the charged-pion background is largely reduced. Performance was evaluated on experimental data, providing a framework to validate such approaches at CLAS12 and future electron-beam facilities.