Speaker
Description
Modern particle-physics detectors require continuous monitoring, rapid anomaly detection, and timely expert response. This contribution presents an Agentic AI framework for intelligent detector operations, integrating sensor information, automated anomaly detection, contextual interpretation, and decision support.
Environmental and operational sensor data are analysed using Agentic AI tools that generate alerts, interpret detector conditions, and provide recommendations to experts and shifters. The framework is also being extended to Data Quality Monitoring through autoencoders applied to detector monitoring histograms.
Agentic AI is used to interpret anomalies in context, correlate information from multiple monitoring sources, and recommend possible actions. This approach represents a step towards an AI-assisted shifter and provides a pathway to intelligent monitoring for current and future high-energy physics facilities.
