高密度环境下的TPC粒子跟踪与识别
TPC tracking and particle identification in high-density environment
摘要：
提出了一种基于卡尔曼滤波的ALICE时间投影室（TPC）航迹发现与拟合算法。讨论了利用d $E$ /d $x$测量实现粒子识别（PID）的方法。滤波和PID算法能够处理非高斯噪声以及高密度环境中的模糊测量。占用率可高达40%，并且由于重叠，通常会丢失沿轨道的点，并且其他点会显著移位。在本算法中，首先，聚类发现和空间点的重建。聚类的形状提供了有关重叠因子的信息。对变形点采用快速样条展开算法。然后，使用关于簇形状和轨道参数的信息来估计期望的空间点误差。此外，使用关于局部轨道重叠的可用信息。在模拟数据集上进行测试，以验证分析并获得算法的实际经验。
英文摘要：
Track finding and fitting algorithm in the ALICE Time projection chamber (TPC) based on Kalman-filtering is presented. Implementation of particle identification (PID) using d$E$/d$x$ measurement is discussed. Filtering and PID algorithm is able to cope with non-Gaussian noise as well as with ambiguous measurements in a high-density environment. The occupancy can reach up to 40% and due to the overlaps, often the points along the track are lost and others are significantly displaced. In the present algorithm, first, clusters are found and the space points are reconstructed. The shape of a cluster provides information about overlap factor. Fast spline unfolding algorithm is applied for points with distorted shapes. Then, the expected space point error is estimated using information about the cluster shape and track parameters. Furthermore, available information about local track overlap is used. Tests are performed on simulation data sets to validate the analysis and to gain practical experience with the algorithm.
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