| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01158 | |
| Number of page(s) | 7 | |
| DOI | https://doi.org/10.1051/epjconf/202533701158 | |
| Published online | 07 October 2025 | |
https://doi.org/10.1051/epjconf/202533701158
Anomaly Detection on BESIII Electromagnetic Calorimeter using Machine Learning
1 Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, China
2 University of Chinese Academy of Sciences, Beijing, China
* e-mail: mrli@ihep.ac.cn
** e-mail: jixb@ihep.ac.cn
*** e-mail: liucx@ihep.ac.cn
Published online: 7 October 2025
In the BESIII, unsupervised anomaly detection method based on autoencoders is applied to the CsI(Tl) electromagnetic calorimeter (EMC). This approach examines histograms of the electronic responses of all crystals, providing more accurate anomaly detection while requiring less personpower than traditional methods.
© The Authors, published by EDP Sciences, 2025
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