大型强子对撞机上快速且精确的带电粒子轨迹回归学习
Fast and Precise Learned Charged-Particle Trajectory Regression at the Large Hadron Collider
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中文总结 AI 辅助
本文提出一种将带电粒子轨迹参数回归视为序列建模的训练方案，利用双向门控线性循环编码器复现经典拟合的十万分之一精度，并在GPU推理中实现更高吞吐量，降低大型强子对撞机模式识别的计算成本。
中文摘要 AI 辅助
我们提出了一种训练方案，将高能物理探测器数据上的带电粒子轨迹参数回归视为序列建模任务。卡尔曼滤波和线性化最小二乘拟合一直是该任务的经典标准方法：它们是稀疏采样线性高斯数据的最优估计器，通常用于轨迹参数回归（拟合）。针对该领域实现的经典拟合技术，通过对探测器几何结构、材料、探测效应的详细建模，以及通过探测器非均匀磁场中运动方程的精确数值积分，达到了$10^5$分之一（即十万分之一）的最终精度。通过本研究，我们证明使用双向门控线性循环编码器，能够复现经典轨迹拟合技术的全部精度。使用自定义内核，我们在GPU推理期间还实现了显著更高的吞吐量，与运行在价格相当的多核CPU服务器（代表通常采用的硬件）上的经典拟合软件相比。这种加速将为大型强子对撞机的模式识别带来可观的成本节约。据我们所知，这是首个达到完全精度且同时提供降低计算成本机会的端到端学习轨迹拟合。
英文摘要
We propose a training recipe that treats charged-particle trajectory parameter regression on high-energy physics detector data as a sequence-modeling task. Kalman filters and linearized least-squares fits have been the classical standard approach for this task: they are optimal estimators for sparsely sampled linear-Gaussian data and are commonly used for trajectory parameter regression (fitting). The classical fitting techniques implemented for this domain reach a final precision of one part in $10^5$ through detailed modeling of detector geometry, material, detection effects and precise numerical integration of the equations of motion through the detector's inhomogeneous magnetic field. With this study, we demonstrate that using a bidirectional gated linear recurrent encoder, one is able to reproduce the full precision of classical track fitting techniques. Using a custom kernel, we also achieve significantly higher throughput during GPU inference, compared to classical fitting software running on similarly priced multi-core CPU servers representing typically employed hardware. Such a speedup would lead to considerable cost savings for the pattern recognition at the Large Hadron Collider. To our knowledge, this is the first end-to-end learned track fit to reach the full precision and, at the same time, offer the opportunity to reduce the computing costs.
