大型强子对撞机全碰撞率下的径迹拟合
Track fitting at the full LHC collision rate
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中文总结 AI 辅助
LHCb实验升级后，提出一种基于卡尔曼滤波器的方法，用于全碰撞率下带电粒子轨迹参数估计。该方法采用快速解析参数化取代复杂计算，在GPU上实现，提高了动量分辨率，增强了对探测器未对准效应的鲁棒性及背景区分能力。
中文摘要 AI 辅助
大型强子对撞机上的LHCb实验在LHC Run 3数据采集期前进行了重大升级，其触发系统采用全软件方法。本文展示了卡尔曼滤波器的快速实现，自2025年数据采集期起用于首个触发阶段，能以30MHz的全LHCb碰撞频率确定带电粒子轨迹的参数估计。该方法用快速解析参数化取代计算昂贵的磁场图查找和数值积分方法，同时保持卡尔曼滤波的数学框架。在一级触发的约500个GPU上实现，算法在实时触发环境中取代了先前的部分径迹拟合算法，处理时间增加2%。与先前的拟合器相比，参数化卡尔曼滤波器的动量分辨率显著提高，重建的D0和J/ψ强子的不变质量分辨率提高了两倍。它还对探测器未对准效应更具鲁棒性，大幅增强了对真实粒子轨迹和偶然背景的区分能力，在不影响真实径迹效率的情况下，将后者的剔除率提高了一倍多。
英文摘要
The LHCb experiment at the Large Hadron Collider underwent a major upgrade before the LHC Run 3 data taking period, employing an all-software approach in its trigger system. Here we present a fast implementation of a Kalman filter, used in the first trigger stage since the 2025 data taking period, allowing to determine parameter estimates of charged-particle trajectories at the full LHCb collision frequency of 30 MHz. This approach replaces computationally expensive magnetic field map lookups and numerical integration methods with fast analytical parameterisations while maintaining the mathematical framework of Kalman filtering. Implemented on approximately 500 GPUs within the first-level trigger, the algorithm has replaced the previous partial track fitting algorithm in the real-time trigger environment at the cost of a 2% increase in processing time. Compared to the previous fitter this parameterised Kalman filter shows a significantly improved momentum resolution, resulting in a factor of two improvement in the invariant mass resolutions for reconstructed D0 and J/ψ hadrons. It additionally demonstrates greater robustness against detector misalignment effects and substantially sharpens the discrimination between genuine particle trajectories and accidental background, more than doubling the rejection of the latter at no cost to genuine-track efficiency, for a standard selection working point.
