Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

This article presents a reinforcement learning approach for optimizing real-time event triggering at the Large Hadron Collider (LHC), addressing the limitations of static, hand-tuned trigger menus. The authors adapt Group-Filtered Policy Optimization (GFPO) for streaming control, achieving significant improvements in signal efficiency and in-tolerance rates for both total transverse energy and anomaly-detection triggers, with gains of up to 56% in real collision data without fine-tuning. This work is significant as it demonstrates the first application of RL for trigger control in real LHC data, potentially enhancing the efficiency of data collection in high-energy physics experiments.