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TrackML: A High Energy Physics Particle Tracking Challenge

Calafiura, Polo and Farrell, Steven and Gray, Heather and Vlimant, Jean-Roch and Innocente, Vincenzo and Salzburger, Andreas and Amrouche, Sabrina and Golling, Tobias and Kiehn, Moritz and Estrade, Victor and Germaint, Cécile and Guyon, Isabelle and Moyse, Ed and Rousseau, David and Yilmaz, Yetkin and Gligorov, Vladimir Vava and Hushchyn, Mikhail and Ustyuzhanin, Andrey (2018) TrackML: A High Energy Physics Particle Tracking Challenge. In: 2018 IEEE 14th International Conference on e-Science (e-Science). IEEE , Piscataway, NJ, p. 344. ISBN 978-1-5386-9156-4. http://resolver.caltech.edu/CaltechAUTHORS:20190314-135749360

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Abstract

To attain its ultimate discovery goals, the luminosity of the Large Hadron Collider at CERN will increase so the amount of additional collisions will reach a level of 200 interaction per bunch crossing, a factor 7 w.r.t the current (2017) luminosity. This will be a challenge for the ATLAS and CMS experiments, in particular for track reconstruction algorithms. In terms of software, the increased combinatorial complexity will have to harnessed without any increase in budget. To engage the Computer Science community to contribute new ideas, we organized a Tracking Machine Learning challenge (TrackML) running on the Kaggle platform from March to June 2018, building on the experience of the successful Higgs Machine Learning challenge in 2014. The data were generated using [ACTS], an open source accurate tracking simulator, featuring a typical all silicon LHC tracking detector, with 10 layers of cylinders and disks. Simulated physics events (Pythia ttbar) overlaid with 200 additional collisions yield typically 10000 tracks (100000 hits) per event. The first lessons from the Accuracy phase of the challenge will be discussed.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/escience.2018.00088DOIArticle
Additional Information:© 2018 IEEE.
Record Number:CaltechAUTHORS:20190314-135749360
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20190314-135749360
Official Citation:P. Calafiura et al., "TrackML: A High Energy Physics Particle Tracking Challenge," 2018 IEEE 14th International Conference on e-Science (e-Science), Amsterdam, 2018, pp. 344-344. doi: 10.1109/eScience.2018.00088
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93829
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:14 Mar 2019 21:03
Last Modified:14 Mar 2019 21:03

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