CMS实验中的模型无关搜索与异常检测
Model-independent searches and anomaly detection at the CMS experiment
- University of California, Santa Barbara（加州大学圣塔芭芭拉分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对LHC无新物理信号，CMS采用五种机器学习异常检测方法在138 fb^-1数据中搜索双喷流共振，并已部署实时无监督算法，无需标签即可验证顶夸克。
AI中文摘要：
CERN LHC上缺乏超越标准模型的物理清晰信号，这促使了不预设特定信号假说的搜索策略。本会议报告介绍了基于机器学习的异常检测，以模型无关的方式搜索新物理。CMS进行的首次此类搜索寻找双喷流共振，其喷流具有轻夸克或胶子引发的喷流所不典型的子结构，使用了五种互补的异常检测方法，应用于$\sqrt{s}=13$ TeV下138 fb$^{-1}$的质子-质子碰撞数据。该技术已直接在数据上得到验证，通过无需标签地恢复顶夸克来确认。该计划也已进入实时阶段：两种无监督算法AXOL1TL和CICADA现在运行于CMS一级触发系统内的现场可编程门阵列上，并在2024年数据采集期间选择了超过四十亿个碰撞事件。最后，概述了将共振异常检测扩展到事件级观测量的方向。
英文摘要：
The absence of a clear signal of physics beyond the standard model at the CERN LHC motivates search strategies that do not presuppose a specific signal hypothesis. This proceedings present machine-learning-based anomaly detection to search for new physics in a model-agnostic way. The first such search by CMS looks for dijet resonances whose jets have substructure atypical of jets initiated by light quarks or gluons, using five complementary anomaly detection methods applied to 138 fb$^{-1}$ of proton-proton collision data at $\sqrt{s} = 13$ TeV. The technique has been validated directly on data by recovering the top quark without the use of labels. The program has also moved into real time: two unsupervised algorithms, AXOL1TL and CICADA, now run on field-programmable gate arrays inside the CMS level-1 trigger and selected more than four billion collision events during 2024 data taking. Finally, the extension of resonant anomaly detection to event-level observables is outlined.
