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Track reconstruction at LHC as a collaborative data challenge use case with RAMP

Amrouche, Sabrina and Braun, Nils and Calafiura, Paolo and Farrell, Steven and Gemmler, Jochen and Germain, Cécile and Gligorov, Vladimir Vava and Golling, Tobias and Gray, Heather and Guyon, Isabelle and Hushchyn, Mikhail and Innocente, Vincenzo and Kégl, Balázs and Neuhaus, Sara and Rousseau, David and Salzburger, Andreas and Ustyuzhanin, Andrei and Vlimant, Jean-Roch and Wessel, Christian and Yilmaz, Yetkin (2017) Track reconstruction at LHC as a collaborative data challenge use case with RAMP. EPJ Web of Conferences, 150 . Art. No. 00015. ISSN 2100-014X.

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Charged particle track reconstruction is a major component of data-processing in high-energy physics experiments such as those at the Large Hadron Collider (LHC), and is foreseen to become more and more challenging with higher collision rates. A simplified two-dimensional version of the track reconstruction problem is set up on a collaborative platform, RAMP, in order for the developers to prototype and test new ideas. A small-scale competition was held during the Connecting The Dots / Intelligent Trackers 2017 (CTDWIT 2017) workshop. Despite the short time scale, a number of different approaches have been developed and compared along a single score metric, which was kept generic enough to accommodate a summarized performance in terms of both efficiency and fake rates.

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Additional Information:© 2017 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: 8 August 2017. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 654168.
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European Research Council (ERC)654168
Record Number:CaltechAUTHORS:20180321-124934830
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Official Citation:Track reconstruction at LHC as a collaborative data challenge use case with RAMP. Sabrina Amrouche, Nils Braun, Paolo Calafiura, Steven Farrell, Jochen Gemmler, Cécile Germain, Vladimir Vava Gligorov, Tobias Golling, Heather Gray, Isabelle Guyon, Mikhail Hushchyn, Vincenzo Innocente, Balázs Kégl, Sara Neuhaus, David Rousseau, Andreas Salzburger, Andrei Ustyuzhanin, Jean-Roch Vlimant, Christian Wessel, Yetkin Yilmaz. EPJ Web Conf. 150 00015 (2017). DOI: 10.1051/epjconf/201715000015
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:85400
Deposited By: Tony Diaz
Deposited On:26 Mar 2018 13:52
Last Modified:03 Oct 2019 19:30

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