Anomaly Detection on BESIII Electromagnetic Calorimeter using Machine Learning
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.
Authors
- Chunxiu Liu (ORCID: https://orcid.org/0000-0003-3718-9364)
- M. H. Li (ORCID: https://orcid.org/0009-0005-3701-8874)
- X. B. Ji
Institutions
- Chinese Academy of Sciences (CN)
- Institute of High Energy Physics (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- EPJ Web of Conferences
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1051/epjconf/202533701158
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00