用强化学习动态调整粒子对撞机触发阈值，提升信号捕捉效率。
Learning to Trigger: Reinforcement Learning at the Large Hadron Collider
- 设计基于强化学习的在线阈值优化算法，实时适应探测器状态变化。
- 在模拟和真实数据上分别实现48%和56%的稳定运行时间提升。
- 首次在真实对撞机数据上验证强化学习触发控制，适合高能物理研究者。
大型强子对撞机等高通量科学设施依赖实时事件筛选（触发）机制，在带宽、延迟和存储限制下运行。目前触发菜单多为静态手工调参，随探测器状态、堆叠效应和背景成分漂移易失效。本文将在线阈值调整建模为序列决策问题：强化学习代理接收近期事件率与信号敏感特征的流式摘要，动态更新触发阈值，在维持背景率在容忍区间内的前提下最大化信号效率。我们改进了分组过滤策略优化（GFPO），提出两种新变体（GFPO-F、GFPO-FR），训练中强制背景率可行性。在模拟对撞机运行基准测试中，针对总横动量（H_T）触发与基于重建误差的异常检测（AD）触发进行评估。在蒙特卡洛流中，代理使处于容忍区间的时长占比分别提升48%（H_T）和28%（AD），且在这些区间内信号效率累计提升最高达2%。迁移至真实碰撞数据（CMS Run 283408）后，无需微调即实现H_T触发56%、AD触发28%的容忍区间改善，并进一步提升信号效率。据我们所知，这是首个在真实对撞机数据上验证强化学习触发控制的案例。代码已开源于https://github.com/Zixind/GFPO_LHC。
原文摘要 · Abstract (English)
High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{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 \emph{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 \emph{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).
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