Conveners
AD for Instrumentation
- Isobel Ojalvo (Princeton University)
Recent efforts in high energy physics have explored a variety of machine learning approaches for detecting anomalies in collision data. While most are designed for offline analysis, applications at the trigger level are emerging. In this work, we introduce a novel framework for autonomous triggering that not only detects anomalous patterns directly at the trigger level but also determines how...
Modern light sources produce too many signals for a small operations team to monitor in real time. As a result, recovering from faults can require long downtimes, or even worse, performance issues may persist undiscovered. Existing automated methods tend to rely on pre-set limits which either miss subtle problems or produce too many false positives. AI methods can solve both problems, but...
