| title | Learning to Trigger: Reinforcement Learning at the Large Hadron Collider |
|---|---|
| authors | Zixin Ding, Shaghayegh Emami, Giovanna Salvi, Cecilia Tosciri, Abhijith Gandrakota, Jennifer Ngadiuba, Nhan Tran, Christian Herwig, David W. Miller, Yuxin Chen |
| date | 2026-06-27 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.23993 |
| affiliation | University of Chicago |
| upvotes | 6 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (triggering) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision-making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal-sensitive features and updates trigger thresholds to maximize signal efficiency while tracking a target background rate within a tolerance band. We adapt Group-Filtered Policy Optimization (GFPO) to streaming control and introduce two variants (GFPO-F, GFPO-FR) that enforce background rate feasibility during training. On a benchmark that emulates realistic collider operation, we study two representative triggers: a total transverse energy (H_{T}) trigger sensitive to pileup variation, and an anomaly-detection (AD) trigger based on reconstruction loss for rare or non-standard signatures. On Monte Carlo streams, our agent increases the fraction of in-tolerance time intervals by 48\% (H_T) and 28\% (AD), with a cumulative gain of up to 2\% in signal efficiency on those in-tolerance intervals. Transferring from simulation to real collision data (CMS Run 283408), the same agent, without fine-tuning, achieves a 56\% (H_T) and 28\% (AD) in-tolerance improvement over baselines, with further signal-efficiency gain on both triggers. To our knowledge, this is the first demonstration of RL-based trigger control on real Large Hadron Collider collision data. Code is available at https://github.com/Zixind/GFPO_LHC (see repo for details). |
cite
@misc{ding2026learning,
title={Learning to Trigger: Reinforcement Learning at the Large Hadron Collider},
author={Zixin Ding and Shaghayegh Emami and Giovanna Salvi and Cecilia Tosciri and Abhijith Gandrakota and Jennifer Ngadiuba and Nhan Tran and Christian Herwig and David W. Miller and Yuxin Chen},
year={2026},
eprint={2606.23993},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.23993}
}