Speaker
Description
The Large Hadron Collider (LHC) produces collision data at the rate of 40 MHz, making it infeasible to store or analyze all events. Conventional trigger strategies at the experiments operating at the LHC typically rely on predefined physics signatures to decide which events to retain. However, possible manifestations of new physics may not conform to these established patterns and thus risk being discarded. Anomaly detection in the trigger menu is essential for identifying rare and potentially new physics events that may evade traditional selection criteria.
We propose a reinforcement learning (RL)-based approach that autonomously learns to detect and respond to anomalous patterns in real time at the trigger level. Our RL agent is trained to identify deviations from expected event distributions and adapt trigger thresholds dynamically based on detector conditions and feedback. Unlike static rule-based systems, the RL agent continuously optimizes its policy to balance trigger bandwidth, compute constraints, and physics sensitivity. This approach enhances the LHC’s ability to capture previously unseen physics processes while maintaining operational stability. Our approach represents a step toward intelligent, scalable trigger systems that maximize discovery potential without relying on rigid and handcrafted selection rules.
