Issue |
EPJ Web Conf.
Volume 214, 2019
23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018)
|
|
---|---|---|
Article Number | 06037 | |
Number of page(s) | 7 | |
Section | T6 - Machine learning & analysis | |
DOI | https://doi.org/10.1051/epjconf/201921406037 | |
Published online | 17 September 2019 |
https://doi.org/10.1051/epjconf/201921406037
The TrackML high-energy physics tracking challenge on Kaggle
1
Département de physique nucléaire et corpusculaire, Université de Genève,
Genève,
Switzerland
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: moritz.kiehn@unige.ch
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
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.