Speaker
Description
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 to respond to them in real time. We develop and benchmark an autonomous, or self-driving trigger framework that integrates anomaly detection with real-time control strategies, dynamically adjusting trigger thresholds and allocations in response to changing conditions. Using feedback-based control and resource-aware optimization that accounts for trigger bandwidth and compute constraints, our system maintains stable trigger rates while enhancing sensitivity to rare or unexpected signals. This work represents a step toward scalable, adaptive trigger systems and highlights new directions that harness the full potential of anomaly detection for real-time data acquisition.
