AI 中文总结
本文针对√s=13.6TeV质子-质子碰撞，研究CMS的HLT系统中基于深度学习的重味喷注鉴别算法，其提升了关键物理过程的信号效率，助力希格斯玻色子相关分析。
AI 中文摘要
CMS触发系统在数据采集阶段发挥关键作用，它将大型强子对撞机（LHC）产生的高碰撞率降低至数千赫兹，以适配数据存储及后续的离线分析。该系统旨在维持对各类过程的高选择效率，其中包括涉及重味夸克（b夸克与c夸克）产生的过程，这类过程在诸多物理分析中具有独特的信号特征。为在实现这一目标的同时维持可持续的触发输出率，研究人员开发了专门的喷注味鉴别方法，并对其进行优化以应用于高级触发（HLT）系统。本文介绍了2022至2024年间，在质心系能量√s=13.6TeV的质子-质子碰撞中，部署于HLT系统内的基于深度学习的喷注鉴别算法的设计、调试及性能表现。这些新算法在多种关键物理过程的信号效率上实现了显著提升，其中包括衰变为四个b夸克的非共振希格斯玻色子对产生过程，以及通过矢量玻色子融合产生的希格斯玻色子、与顶夸克-反顶夸克对（t$\bar{t}$）伴随产生的希格斯玻色子，且涉及H→b$\bar{b}$和H→c$\bar{c}$衰变道。
英文摘要
The CMS trigger system plays a crucial role during data taking, reducing the large collision rate delivered by the LHC to a few kHz for data storage and subsequent offline analysis. The system aims to maintain a high selection efficiency for a wide range of processes, including those involving jets originating from heavy-flavour quarks (b and c), which provide a distinctive signature in many physics analyses. To achieve this while maintaining a sustainable trigger output rate, dedicated jet flavour identification methods are developed and optimized for use in the high-level trigger (HLT). This paper presents the design, commissioning, and performance of deep-learning-based jet identification algorithms deployed in the HLT during 2022$-$2024, for proton-proton collisions at $\sqrt{s}$ = 13.6 TeV. The new algorithms enabled significant improvements in signal efficiency for a variety of key physics processes, including the non-resonant production of Higgs boson pairs decaying to four b quarks, as well as Higgs boson production via both vector boson fusion and in association with a $\mathrm{t\bar{t}}$ pair, in the H $\to$ $\mathrm{b\bar{b}}$ and H $\to$ $\mathrm{c\bar{c}}$ decay channels.
CommentsSubmitted to the Journal of High Energy Physics. All figures and tables can be found at http://cms-results.web.cern.ch/cms-results/public-results/publications/BTV-25-002 (CMS Public Pages)
