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
- M. H. Li (ORCID: https://orcid.org/0009-0005-3701-8874)
- Xiaobin Ji
- Chunxiu Liu
Publication Details
- Journal
- Springer Link (Chiba Institute of Technology)
- Published
- 2025-10-07
- DOI
- https://doi.org/10.1051/epjconf/202533701158/pdf
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00