The Tracking Machine Learning Challenge: Throughput Phase

• DOI: 10.1007/s41781-023-00094-w
• Publication Date: 2023-02-13T12:33:24Z
• Authors: Sabrina Amrouche; Laurent Basara; Paolo Calafiura; Dmitry Emeliyanov; Victor Estrade; Steven Farrell; Cécile Germain; Vladimir Vava Gli...; Tobias Golling; Sergey Gorbunov; Heather Gray; Isabelle Guyon; Mikhail Hushchyn; Vincenzo Innocente; Moritz Kiehn; Marcel Kunze; Edward Moyse; David Rousseau; Andreas Salzburger; Andrey Ustyuzhanin; Jean-Roch Vlimant
• Keywords: FOS: Computer and information sciences; 570; Computer Science - Machine Learning; hep-ex; cs.LG; FOS: Physical sciences; 16. Peace & justice; 530; 01 natural sciences; [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; High Energy Physics - Experiment; Machine Learning (cs.LG); High Energy Physics - Experiment (hep-ex); 0103 physical sciences; [PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]; Challenges

AbstractThis 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 $$O(10^5)$$

O (

10 5

)

points, the participants had to connect them into $$O(10^4)$$

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 is analysed in depth and lessons derived.