发表机构
Old Dominion University; Université Paris-Saclay, CEA, IRFU; SUPA, School of Physics and Astronomy, University of Glasgow（老道明大学; 巴黎萨克雷大学、法国原子能委员会、IRFU; 苏格兰物理天文学联盟、格拉斯哥大学物理与天文学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对CLAS12实验开发两种不同输入特征的BDT粒子鉴别模型，经模拟与实验数据验证，可保留超90%轻子并大幅降低带电π介子背景，为相关装置的方法验证提供框架。
AI 中文摘要
本文提出了CLAS12实验中用于电子和正电子的机器学习粒子鉴别算法，主要目标是最小化实验和模拟数据中的带电π介子污染。我们开发、评估并验证了两个输入特征不同的BDT模型，均在模拟样本上训练，同时使用模拟和实验数据进行严格验证以确保可靠性。结果显示这些模型在减轻背景污染方面有效，在保留模拟样本中超过90%轻子的同时，大幅降低了带电π介子背景。对实验数据的性能评估为CLAS12及未来电子束装置验证此类方法提供了框架。
英文摘要
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.
