PoS - Proceedings of Science
Volume 340 - The 39th International Conference on High Energy Physics (ICHEP2018) - Parallel: Computing and Data Handling
TrackML : a tracking Machine Learning challenge
T. Golling,* S. Amrouche, M. Kiehn, P. Calafiura, S. Farrell, H.M. Gray, V. Estrade, C. Germain, V. Gligorov, I. Guyon, M. Hushchyn, A. Ustyuzhanin, V. Innocente, A. Salzburger, E. Moyse, D. Rousseau, Y. Yilmaz, J.r. Vlimant
*corresponding author
Full text: pdf
Published on: August 02, 2019
The High-Luminosity LHC will see pileup levels reaching 200, which will greatly increase the complexity of the tracking component of the event reconstruction. To reach out to Computer Science specialists, a Tracking Machine Learning challenge (TrackML) was set up on Kaggle in 2018 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 dataset consisting of an accurate simulation of a 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 data set is large to allow for appropriate training of Machine Learning methods: about 100.000 events, 1 billion tracks, 100 GigaByte. The participants of the challenge are asked to find the tracks, which means to build the list of 3D points belonging to each track (deriving the track parameters is not the topic of the challenge). Here the first lessons from the challenge are discussed, including the initial analysis of submitted results.
DOI: https://doi.org/10.22323/1.340.0159
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