Statistical algorithms for particle trajectography

TL;DR

This paper introduces GEM, a computationally efficient algorithm for particle trajectography that extends EM algorithms to handle linear, circular, and mixed particle tracks in arbitrary dimensions, with practical applications demonstrated.

Contribution

The paper presents GEM, a novel, lower-complexity algorithm extending EM for Gaussian mixtures to particle tracking, including circular and mixed tracks, implemented in open-source software.

Findings

GEM reduces computational complexity compared to traditional methods.

It effectively handles linear, circular, and mixed particle tracks.

Applications show practical utility on real particle tracker data.

Abstract

The various algorithms used to extrapolate particle trajectories from measurements are often very time-consuming with computational complexities which are typically quadratic. In this article, we propose a new algorithm called GEM with a lower complexity and reasonable performance on linear tracks. It is an extension of the EM algorithm used to fit Gaussian mixtures. It works in arbitrary dimension and with an arbitrary number of simultaneous particles. In a second part, we extend it to circular tracks (for charged particles) and even a mix of linear and circular tracks. This algorithm is implemented in an open-source library called libgem and two applications are proposed, based on data-sets from two kind of particle trackers.