学习触发：大型强子对撞机中的强化学习

摘要

像大型强子对撞机这类高通量科学设施，依赖实时事件过滤（触发）在带宽、延迟和存储的严格约束下运行。在实际应用中，触发菜单大多是静态且通过手动调优的，随着探测器条件、堆积和背景成分随时间漂移，它们会逐渐变得非最优。我们将在线阈值调优建模为序列决策问题：强化学习智能体吸收流式汇总的近期速率和信号敏感特征，动态更新触发阈值，以在追踪目标背景率的同时最大化信号效率（允许一定容差范围）。我们将分组滤波策略优化（GFPO）适配到流式控制场景，并引入两个变体（GFPO-F、GFPO-FR），在训练过程中强制执行背景率的可行性。在模拟真实对撞机运行的基准测试中，我们研究了两种典型触发：对堆积变化敏感的总横能量（H_T）触发，以及基于重构损失检测罕见或非标准信号的异常检测（AD）触发。在蒙特卡洛数据流上，我们的智能体将时间区间内处于容差范围内的比例提升了48%（H_T）和28%（AD），并在这些容差区间内的信号效率上获得了高达2%的累积增益。从模拟迁移到真实碰撞数据（CMS Run 283408）时，同一智能体无需微调，相较于基线方法在容差范围内提升了56%（H_T）和28%（AD），两种触发器的信号效率均进一步提升。据我们所知，这是首次在真实大型强子对撞机碰撞数据上实现基于强化学习的触发控制。代码见 https://github.com/Zixind/GFPO_LHC （详情参见仓库）。

English

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).