Speaker
Description
Machine learning has opened new possibilities for detecting anomalous signatures in high-energy physics data. While most approaches have focused on offline use, there is growing interest in applying these tools directly at the trigger level to enhance discovery potential. In this work, we present a novel framework for autonomous triggering that not only detects anomalous patterns in real time but also determines how to respond to them. We develop and benchmark a self-driving trigger system that integrates anomaly detection with real-time control strategies, dynamically adjusting trigger thresholds and resource allocations in response to changing beam conditions. Using CMS Open Data and a realistic benchmarking setup, our system employs feedback-based control and resource-aware optimization that accounts for trigger bandwidth and compute constraints to maintain stable trigger rates while enhancing sensitivity to rare or unexpected signals. This approach represents a step toward adaptive, intelligent trigger systems for high-throughput experimental environments.
