Work

Anomaly Detection on BESIII Electromagnetic Calorimeter using Machine Learning

Year: 2025

Type: conference-paper

Abstract: 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.

Source: EPJ Web of Conferences

Authors: Mingrun Li, Xiaobin Ji, Chunxiu Liu

Institutions: Chinese Academy of Sciences, Institute of High Energy Physics, University of Chinese Academy of Sciences

Language: English

Topic: Particle Detector Development and Performance

Subfield: Nuclear and High Energy Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W4414882380

FWCI:

Cites: 7

Cited by:

Related to: