TPC tracking and particle identification in high-density environment

TL;DR

This paper presents a Kalman-filter-based tracking and particle identification algorithm for the ALICE TPC that effectively handles high-density environments with overlapping signals and non-Gaussian noise.

Contribution

It introduces a novel algorithm capable of accurate track reconstruction and PID in environments with up to 40% occupancy and overlapping clusters.

Findings

Algorithm successfully reconstructs tracks with high accuracy.

Particle identification using dE/dx is effectively integrated.

Validated on simulation data with promising results.

Abstract

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.