用深度集合模型精准识别粒子喷注起源，提升高亮度下多喷注事件筛选效率。
Deep sets and event-level maximum-likelihood estimation for fast pile-up jet rejection in ATLAS
- 基于深度集合架构的DIPz模型，通过关联轨迹预测喷注沿束流线的位置。
- 事件级判别器MLPL在80次堆叠碰撞下实现高效多喷注信号筛选。
- 适合实时触发系统，兼顾准确率与计算速度，适用于未来高亮度对撞机。
LHC每束团交叉时发生多重质子-质子碰撞（堆叠），预计运行3阶段平均相互作用数达80，高亮度期可达200。由此导致多喷注事例率急剧上升。为应对更高亮度，需在触发层级高效区分喷注来源。本文提出一种基于Deep Sets架构的不确定性感知喷注回归模型DIPz，输入为每个喷注关联的带电粒子轨迹，回归其沿束流线的起源位置。构建事件级判别器最大对数似然乘积（MLPL），结合各喷注预测结果，经剪裁优化以筛选符合目标多喷注信号的选择。该方法在多喷注末态中实现了鲁棒且高效的堆叠排斥，适用于ATLAS高能级触发系统的实时事件选择。
原文摘要 · Abstract (English)
Multiple proton-proton collisions (pile-up) occur at every bunch crossing at the LHC, with the mean number of interactions expected to reach 80 during Run 3 and up to 200 at the High-Luminosity LHC. As a direct consequence, events with multijet signatures will occur at increasingly high rates. To cope with the increased luminosity, being able to efficiently group jets according to their origin along the beamline is crucial, particularly at the trigger level. In this work, a novel uncertainty-aware jet regression model based on a Deep Sets architecture is introduced, DIPz, to regress on a jet origin position along the beamline. The inputs to the DIPz algorithm are the charged particle tracks associated to each jet. An event-level discriminant, the Maximum Log Product of Likelihoods (MLPL), is constructed by combining the DIPz per-jet predictions. MLPL is cut-optimized to select events compatible with targeted multi-jet signature selection. This combined approach provides a robust and computationally efficient method for pile-up rejection in multi-jet final states, applicable to real-time event selections at the ATLAS High Level Trigger.
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