AI 中文总结
研究大型强子对撞机底夸克探测器拓扑b触发中神经网络与粒子寿命相关性问题，提出两种减轻相关性的方法，并评估所得模型性能。
AI 中文摘要
大型强子对撞机底夸克探测器拓扑美触发是基于软件的大型强子对撞机底夸克探测器中选择含b重子碰撞事件的主要算法集。该算法应用单调利普希茨神经网络来选择与b衰变独特拓扑一致的带电粒子顶点，即那些具有长寿命和横向动量的顶点。许多对记录事件的分析要求在长寿命时选择对b重子寿命无偏。在更繁忙的探测器环境中准确重建具有挑战性，此时每束交叉会同时发生几次可见质子 - 质子碰撞，衰变产物的错误关联会导致测得寿命人为增大的顶点。本文提出两种方法来减轻长寿命时神经网络分数与候选寿命之间的相关性，并评估所得模型的性能。
英文摘要
The LHCb topological beauty trigger is the primary set of algorithms for selecting collision events containing $b$-hadrons in the fully software-based LHCb trigger. The algorithms apply monotonic Lipschitz neural networks (NNs) to select vertices of charged particles consistent with the distinct topology of a $b$ decay, i.e., those with large lifetimes and transverse momentum. Many analyses of the events recorded require that the selection must be unbiased with respect to the $b$-hadron lifetime at large lifetimes. Accurate reconstruction is challenging in busier detector environments, in which several visible proton-proton collisions occur simultaneously per bunch crossing, such that misassociation of decay products can result in vertices with artificially large measured lifetimes. This paper presents two approaches to mitigate correlations between NN scores and candidate lifetimes at large lifetime, and evaluates the performance of the resulting models.
Comments9 pages, 5 figures. As submitted to European Physical Journal C, Reviewed by Diego Martínez Santos and Jacco De Vries
