The Tracking Machine Learning challenge : Accuracy phase
Abstract
This paper reports the results of an experiment in high energy physics: using the power of the "crowd" to solve difficult experimental problems linked to tracking accurately the trajectory of particles in the Large Hadron Collider (LHC). This experiment took the form of a machine learning challenge organized in 2018: the Tracking Machine Learning Challenge (TrackML). Its results were discussed at the competition session at the Neural Information Processing Systems conference (NeurIPS 2018). Given 100.000 points, the participants had to connect them into about 10.000 arcs of circles, following the trajectory of particles issued from very high energy proton collisions. The competition was difficult with a dozen front-runners well ahead of a pack. The single competition score is shown to be accurate and effective in selecting the best algorithms from the domain point of view. The competition has exposed a diversity of approaches, with various roles for Machine Learning, a number of which are discussed in the document
Keywords
Cite
@article{arxiv.1904.06778,
title = {The Tracking Machine Learning challenge : Accuracy phase},
author = {Sabrina Amrouche and Laurent Basara and Paolo Calafiura and Victor Estrade and Steven Farrell and Diogo R. Ferreira and Liam Finnie and Nicole Finnie and Cécile Germain and Vladimir Vava Gligorov and Tobias Golling and Sergey Gorbunov and Heather Gray and Isabelle Guyon and Mikhail Hushchyn and Vincenzo Innocente and Moritz Kiehn and Edward Moyse and Jean-Francois Puget and Yuval Reina and David Rousseau and Andreas Salzburger and Andrey Ustyuzhanin and Jean-Roch Vlimant and Johan Sokrates Wind and Trian Xylouris and Yetkin Yilmaz},
journal= {arXiv preprint arXiv:1904.06778},
year = {2021}
}
Comments
36 pages, 22 figures