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Multi-Hypothesis Tracking of charged particles through drift tube arrays

机译:通过漂移管阵列的多假设跟踪带电粒子

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We report on the application of techniques from the target tracking literature to the experimental study of the distribution of charged particles. In the experiment we have multiple planes of position-sensitive detectors able to detect locations of particle hits in each sensitive plane. The problem we address here is determining how to combine a series of particular hits into a particle trajectory. This simple task is complicated by the specifics of the measurements, missed hits and background. In the target tracking literature, the challenge of associating hits in different images is called the association problem, and current best practice is Multi-Hypothesis Tracking or MHT. Recent work has investigated the possibility of using Markov chain Monte Carlo (MCMC) methods for addressing the multi-hypothesis challenge. In our study we report on results from both approaches and on the trade off between computational budget and accuracy. We extend the usual MHT formalism to select tracks with certain qualities and set quality flags as required by the experiment. In our presentation we describe in more detail our approach and preliminary results of its application to the experimental data.
机译:我们报告了从目标跟踪文献到带电粒子分布的实验研究技术的应用。在实验中,我们有多个位置敏感检测器平面,它们能够检测每个敏感平面中粒子命中的位置。我们在此解决的问题是确定如何将一系列特定的命中组合到粒子轨迹中。测量的细节,未命中的结果和背景使这项简单的任务变得复杂。在目标跟踪文献中,将不同图像中的命中关联的挑战称为关联问题,当前的最佳实践是多假设跟踪或MHT。最近的工作研究了使用马尔可夫链蒙特卡罗(MCMC)方法解决多重假设挑战的可能性。在我们的研究中,我们报告了两种方法的结果以及计算预算和准确性之间的权衡。我们扩展了通常的MHT形式主义,以选择具有一定质量的音轨,并根据实验要求设置质量标记。在我们的演示文稿中,我们将更详细地描述我们的方法以及将其应用于实验数据的初步结果。

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