The Tracking Machine Learning challenge : Throughput phase

TL;DR

This paper discusses the second phase of the Tracking Machine Learning challenge, focusing on balancing accuracy and inference speed in particle trajectory tracking, with top solutions significantly outperforming previous methods.

Contribution

It introduces a new challenge phase emphasizing speed-accuracy trade-offs and analyzes diverse algorithmic approaches achieving superior performance.

Findings

Top solutions are an order of magnitude faster than previous state-of-the-art.

Diverse techniques were employed, providing insights into effective tracking algorithms.

Performance analysis yields lessons for future particle tracking methods.

Abstract

This paper reports on the second "Throughput" phase of the Tracking Machine Learning (TrackML) challenge on the Codalab platform. As in the first "Accuracy" phase, the participants had to solve a difficult experimental problem linked to tracking accurately the trajectory of particles as e.g. created at the Large Hadron Collider (LHC): given O($10^5$) points, the participants had to connect them into O($10^4$) individual groups that represent the particle trajectories which are approximated helical. While in the first phase only the accuracy mattered, the goal of this second phase was a compromise between the accuracy and the speed of inference. Both were measured on the Codalab platform where the participants had to upload their software. The best three participants had solutions with good accuracy and speed an order of magnitude faster than the state of the art when the challenge was designed. Although the core algorithms were less diverse than in the first phase, a diversity of techniques have been used and are described in this paper. The performance of the algorithms are analysed in depth and lessons derived.