The TrackML high-energy physics tracking challenge on Kaggle
Département de physique nucléaire et corpusculaire, Université de Genève,
2 Physics Division, Lawrence Berkeley National Laboratory and University of California, Berkeley CA, USA
3 LRI/TAU, Université Paris-Sud/INRIA/CNRS, Université Paris-Saclay, Gif-sur-Yvette, France
4 LPNHE, Sorbonne Université, Paris Diderot Sorbonne Paris Citè, CNRS/IN2P3, Paris, France
5 UPSud/INRIA, Universitè Paris-Saclay, Orsay, France
6 ChaLearn California, USA
7 National Research University Higher School of Economics Moscow, Russia
8 Yandex School of Data Analysis, Moscow, Russia
9 CERN Geneva, Switzerland
10 Department of Physics, University of Massachusetts, Amherst MA, U.S.A.
11 LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France
12 California Institute of Technology, Pasadena CA, USA
* e-mail: email@example.com
Published online: 17 September 2019
The High-Luminosity LHC (HL-LHC) is expected to reach unprecedented collision intensities, which in turn will greatly increase the complexity of tracking within the event reconstruction. To reach out to computer science specialists, a tracking machine learning challenge (TrackML) was set up on Kaggle by a team of ATLAS, CMS, and LHCb physicists tracking experts and computer scientists building on the experience of the successful Higgs Machine Learning challenge in 2014. A training dataset based on a simulation of a generic HL-LHC experiment tracker has been created, listing for each event the measured 3D points, and the list of 3D points associated to a true track.The participants to the challenge should find the tracks in the test dataset, which means building the list of 3D points belonging to each track.The emphasis is to expose innovative approaches, rather than hyper-optimising known approaches. A metric reflecting the accuracy of a model at finding the proper associations that matter most to physics analysis will allow to select good candidates to augment or replace existing algorithms.
© The Authors, published by EDP Sciences, 2019
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