论文标题
在CLAS12检测器中使用机器学习进行粒子轨迹识别
Using Machine Learning for Particle Track Identification in the CLAS12 Detector
论文作者
论文摘要
粒子跟踪重建是核物理实验中计算中最密集的过程。传统算法使用一种组合方法，该方法详尽地测试轨道测量值（“ hits”）来识别形成实际粒子轨迹的轨迹。在本文中，我们描述了四个机器学习（ML）模型的开发，这些模型通过从漂移室中的测量值中识别出有效的跟踪候选者来帮助跟踪算法。测试了几种类型的机器学习模型，包括：卷积神经网络（CNN），多层感知器（MLP），极为随机的树（ERT）和复发性神经网络（RNN）。这项工作的结果是，MLP网络分类器是CLAS12重建软件的一部分，以提供推荐的跟踪候选者的跟踪代码。与现有算法相比，所得软件的准确性大于99 \％，并导致端到端的速度为35 \％。
Particle track reconstruction is the most computationally intensive process in nuclear physics experiments. Traditional algorithms use a combinatorial approach that exhaustively tests track measurements ("hits") to identify those that form an actual particle trajectory. In this article, we describe the development of four machine learning (ML) models that assist the tracking algorithm by identifying valid track candidates from the measurements in drift chambers. Several types of machine learning models were tested, including: Convolutional Neural Networks (CNN), Multi-Layer Perceptrons (MLP), Extremely Randomized Trees (ERT) and Recurrent Neural Networks (RNN). As a result of this work, an MLP network classifier was implemented as part of the CLAS12 reconstruction software to provide the tracking code with recommended track candidates. The resulting software achieved accuracy of greater than 99\% and resulted in an end-to-end speedup of 35\% compared to existing algorithms.
