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TrackML high-energy physics tracking challenge on Kaggle

Kiehn, Moritz and Amrouche, Sabrina and Calafiura, Paolo and Estrade, Victor and Farrell, Steven and Germain, Cécile and Gligorov, Vava and Golling, Tobias and Gray, Heather and Guyon, Isabelle and Hushchyn, Mikhail and Innocente, Vincenzo and Moyse, Edward and Rousseau, David and Salzburger, Andreas and Ustyuzhanin, Andrey and Vlimant, Jean-Roch and Yilnaz, Yetkin (2019) TrackML high-energy physics tracking challenge on Kaggle. EPJ Web of Conferences, 214 . Art. No. 06037. ISSN 2100-014X. https://resolver.caltech.edu/CaltechAUTHORS:20200309-153110762

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Abstract

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1051/epjconf/201921406037DOIArticle
Additional Information:© 2019 The Authors, published by EDP Sciences. 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. Published online 17 September 2019. The team would like to thank CERN for allowing the use of the dataset, and Kaggle for hosting it. We are very grateful to our generous sponsors without which the challenges would not have been possible. Platinum sponsors: Kaggle, Nvidia, and Université de Genève. Gold sponsors: Chalearn and DataIA. Silver sponsors: CERN Openlab, Paris-Saclay CDS, INRIA, ERC mPP, ERC RECEPT, Common Ground, Université Paris-Sud, INQNET, Fermilab, and pyTorch. TG acknowledges the support of the Swiss National Science Foundation under the grant 200020_181984. This project has received funding from the European Union Horizon 2020 research and innovation programme under grant agreement No 724777 “RECEPT”, No 772369 “mPP” and No 654168 “AIDA-2020”.
Funders:
Funding AgencyGrant Number
Swiss National Science Foundation (SNSF)200020_181984
European Research Council (ERC)724777
European Research Council (ERC)772369
European Research Council (ERC)654168
Record Number:CaltechAUTHORS:20200309-153110762
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200309-153110762
Official Citation:The TrackML high-energy physics tracking challenge on Kaggle. Moritz Kiehn, Sabrina Amrouche, Paolo Calafiura, Victor Estrade, Steven Farrell, Cécile Germain, Vava Gligorov, Tobias Golling, Heather Gray, Isabelle Guyon, Mikhail Hushchyn, Vincenzo Innocente, Edward Moyse, David Rousseau, Andreas Salzburger, Andrey Ustyuzhanin, Jean-Roch Vlimant and Yetkin Yilnaz. EPJ Web Conf., 214 (2019) 06037; DOI: https://doi.org/10.1051/epjconf/201921406037
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:101797
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:09 Mar 2020 22:40
Last Modified:09 Mar 2020 22:40

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