首页|Low-Latency Beam-Background Suppression for the BESIII Trigger System Using Machine Learning Trained on Experimental Data
Low-Latency Beam-Background Suppression for the BESIII Trigger System Using Machine Learning Trained on Experimental Data
Zhang, Mr. Hanlin Li, Prof. Ke Yin, Prof. Junhao Chen, Prof. Boping Zhao, Dr. Jingzhou Gong, Prof. Wenxuan Zhao, Prof. Minggang
Low-Latency Beam-Background Suppression for the BESIII Trigger System Using Machine Learning Trained on Experimental Data
Low-Latency Beam-Background Suppression for the BESIII Trigger System Using Machine Learning Trained on Experimental Data
摘要
Beam-induced backgrounds contribute to the trigger and data-acquisition rate at high-luminosity electron--positron colliders. Their dependence on beam and machine conditions motivates data-driven studies alongside simulation. A data-driven machine-learning method for trigger-level beam-background suppression is developed using experimental data recorded by BESIII.Compact neural-network classifiers are trained and evaluated for two trigger configurations: one based only on tracking and time-of-flight information, and one based only on electromagnetic-calorimeter information.Each classifier uses 48 binary Level-1 trigger conditions of the BESIII detector.The models distinguish hadron events from beam-background events and are evaluated against the established trigger selection at matched hadron-event efficiency. Relative to the established trigger selection, the beam-background false-positive rate is reduced by 19--60\% across the studied configurations and center-of-mass energies, with stable suppression performance as tested using random-trigger data.Hardware-oriented synthesis indicates that the compact network is suitable for low-latency FPGA implementation. The proposed method can reduce the fraction of beam-background events in the trigger stream in future BESIII trigger upgrades. It also provides a reference for trigger optimization at future high-luminosity electron--positron colliders.