Speaker
Description
Efficient online monitoring of the data quality and the detector control system is essential for the smooth operation of any high-energy physics experiment. However, much of this responsibility still relies on manual shifter activity. To reduce workload and increase reliability, we explored artificial intelligence methods that automatically detect unusual patterns in monitoring plots and checklist data from the AMBER experiment. Machine learning techniques—including k-Nearest Neighbors, One-Class SVMs, and DBSCAN—were tested on data from the 2024 run period. All methods consistently identified anomalous behavior, demonstrating their usage in real-time flagging potential detector issues.
Building on these results, we plan to deploy an AI-assisted checklist analysis, where adaptive models learn the normal operating states of the experiment—for all subsystems—and highlight deviations, while also suggesting whether an anomaly is due to input error, transient fluctuations, or a possible hardware problem. This additional information shall provide shifters with clear warnings and hints to the underlying issues and create automated summary reports to ease data-quality assessment and debugging.
As a next step, deploying deep-learning models, such as autoencoders, for more robust anomaly detection and integrating AI into online filtering and detector alignment is intended.
CERN group/ Experiment
AMBER experiment
| Working area | Area 3: AI for metadata analysis |
|---|---|
| Project goals | Deployment of an AI-assisted checklist analysis for shifter support |
| Timeline | 6 month for deployment in experiment + 1 year for extension to a flexible framework |
| Available person power | 0.2 FTE (PhD) |
| Additional person power request | 0.5 FTE (fellow/PhD) |
| Is this an already ongoing activity? | Yes |
| Indicative hardware resources needs | interactive GPU cluster |