Low-Latency Beam-Background Suppression for the BESIII Trigger System Using Machine Learning Trained on Experimental Data
Abstract
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 20-60% 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.
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BibTeX
@misc{zhang2026lowlatency,
title = {{Low-Latency Beam-Background Suppression for the BESIII Trigger System Using Machine Learning Trained on Experimental Data}},
author = {Hanlin Zhang and Minggang Zhao and Junhao Yin and Boping Chen and Jingzhou Zhao and Wenxuan Gong and Ke Li},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.26148},
eprint = {2610.26148},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.26148},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Low-Latency Beam-Background Suppression for the BESIII Trigger System Using Machine Learning Trained on Experimental Data
AU - Hanlin Zhang
AU - Minggang Zhao
AU - Junhao Yin
AU - Boping Chen
AU - Jingzhou Zhao
AU - Wenxuan Gong
AU - Ke Li
PY - 2026
DA - 2026/10/19
PB - arXiv
DO - 10.48550/arxiv.2610.26148
UR - https://arxiv.org/abs/2610.26148
AB - 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 20-60% 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.
ER -