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arXiv 2601.08910physics.ins-detcs.AIhep-ex
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迈向LHC的自驱动触发:实时适应性响应

Towards a Self-Driving Trigger at the LHC: Adaptive Response in Real Time

University of Michigan(密歇根大学);Enrico Fermi Institute(恩里科·费米研究所);University of Chicago(芝加哥大学);Fermi National Accelerator Laboratory(费米国家加速器实验室);Department of Physics, Kavli Institute for Cosmological Physics(物理系,宇宙学物理研究所)

Shaghayegh Emami, Cecilia Tosciri, Giovanna Salvi, Zixin Ding, Yuxin Chen, Abhijith Gandrakota, Christian Herwig, David W. Miller, Jennifer Ngadiuba, Nhan Tran

AI总结:

本文提出了一种自驱动触发系统,通过实时动态调整资源和阈值,利用机器学习优化高能物理实验中的触发性能,提升信号效率和计算效率。

摘要:

在高吞吐量科学设施中,如大型强子对撞机(LHC)的实验,实时数据过滤和选择——或触发——系统必须在严格的带宽、延迟和存储限制下处理极高的数据流速率。然而,这些系统通常设计为静态的、手动调整的筛选标准菜单,基于先前的知识和模拟。在本工作中,我们进一步探索自驱动触发的概念,即一个自主的数据过滤框架,能够实时重新分配资源并调整阈值,以优化信号效率、速率稳定性和计算成本,随着仪器和环境条件的变化。我们引入了一个基准生态系统来模拟真实的对撞机场景,并展示了实时优化一个包含典型能量和触发的菜单以及利用机器学习的现代异常检测算法的能力,这些算法针对非标准事件拓扑结构。使用模拟数据流和公开可用的紧凑μ子线圈(CMS)实验碰撞数据,我们展示了在特定成本目标下动态和自动优化触发性能的能力,而无需手动重新调整。我们的自适应策略将触发设计从静态菜单的启发式调整转向智能、自动、数据驱动的控制,解锁未来高能物理分析中更大的灵活性和发现潜力。

英文摘要:

Real-time data filtering and selection -- or trigger -- systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider (LHC) must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid (CMS) experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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