2nd GSI/FAIR AI Workshop

The CBM experiment is a complex detector system composed of numerous subdetectors operating simultaneously and generating thousands of monitoring plots to ensure stable and reliable performance. Given the massive data volumes expected during data taking, continuous verification of data quality and the prompt identification of technical faults are essential. This project proposes the adoption of an AI-based automatic anomaly detection framework, HYDRA, originally developed for the GlueX experiment at Jefferson Lab. HYDRA employs computer-vision models for near–real-time image classification to recognize irregular patterns in detector monitoring outputs. The project focuses on adapting and deploying HYDRA for CBM, including the training and maintenance of dedicated models for individual subdetectors such as STS and MUCH, as well as improving HYDRA capabilities to facilitate unsupervised learning using Autoencoders.