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The Tracking Machine Learning challenge : Throughput phase
/ Amrouche, Sabrina (Geneva U.) ; Basara, Laurent (LRI, Paris 11 ; INRIA, Saclay) ; Calafiura, Paolo (LBL, Berkeley ; UC, Berkeley (main) ; UC, Berkeley) ; Emeliyanov, Dmitry (Rutherford) ; Estrade, Victor (LRI, Paris 11 ; INRIA, Saclay) ; Farrell, Steven (LBL, Berkeley ; UC, Berkeley (main) ; UC, Berkeley) ; Germain, Cécile (LRI, Paris 11 ; INRIA, Saclay) ; Gligorov, Vladimir Vava (LPNHE, Paris) ; Golling, Tobias (Geneva U.) ; Gorbunov, Sergey (Goethe U., Frankfurt (main)) et al.
This paper reports on the second "Throughput" phase of the Tracking Machine Learning (TrackML) challenge on the Codalab platform. As in the first "Accuracy" phase, the participants had to solve a difficult experimental problem linked to tracking accurately the trajectory of particles as e.g. [...]
arXiv:2105.01160.-
2023-02-13 - 19 p.
- Published in : Comput. Softw. Big Sci. 7 (2023) 1
Fulltext: 2105.01160 - PDF; Publication - PDF;
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TrackML : a tracking Machine Learning challenge
/ Golling, Tobias (Geneva U.) ; Amrouche, Sabrina (Geneva U.) ; Kiehn, Moritz (Geneva U.) ; Calafiura, Paolo (LBL, Berkeley) ; Farrell, Steven (LBL, Berkeley) ; Gray, Heather M (LBL, Berkeley) ; Estrade, Victor (U. Paris-Saclay) ; Germain, Cécile (U. Paris-Saclay) ; Gligorov, Vava (Paris U., VI-VII) ; Guyon, Isabelle (INRIA, Saclay) et al.
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. [...]
SISSA, 2019 - 4 p.
- Published in : PoS ICHEP2018 (2019) 159
Fulltext: PDF;
In : XXXIX International Conference on High Energy Physics, Seoul, Korea, 4 - 11 Jul 2018, pp.159
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TrackML: A High Energy Physics Particle Tracking Challenge
/ Calafiura, Polo (LBNL, Berkeley) ; Farrell, Steven (LBNL, Berkeley) ; Gray, Heather (LBNL, Berkeley) ; Vlimant, Jean-Roch (Caltech) ; Innocente, Vincenzo (CERN) ; Salzburger, Andreas (CERN) ; Amrouche, Sabrina (Geneva U.) ; Golling, Tobias (Geneva U.) ; Kiehn, Moritz (Geneva U.) ; Estrade, Victor (LRI, Paris 11) et al.
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. [...]
2018 - 1 p.
- Published in : 10.1109/eScience.2018.00088
In : 14th eScience IEEE International Conference, Amsterdam, Netherlands, 29 Oct - 1 Nov 2018, pp.344
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The Tracking Machine Learning challenge : Accuracy phase
/ Amrouche, Sabrina (Geneva U.) ; Basara, Laurent (LRI, Paris 11) ; Calafiura, Paolo (LBL, Berkeley) ; Estrade, Victor (LRI, Paris 11) ; Farrell, Steven (LBL, Berkeley) ; Ferreira, Diogo R. (Lisbon, IST) ; Finnie, Liam (IBM, Boblingen) ; Finnie, Nicole (CFEL, Hamburg) ; Germain, Cécile (LRI, Paris 11) ; Gligorov, Vladimir Vava (Paris U., VI-VII) et al.
This paper reports the results of an experiment in high energy physics: using the power of the "crowd" to solve difficult experimental problems linked to tracking accurately the trajectory of particles in the Large Hadron Collider (LHC). This experiment took the form of a machine learning challenge organized in 2018: the Tracking Machine Learning Challenge (TrackML) [...]
arXiv:1904.06778.-
2020 - 36 p.
- Published in : 10.1007/978-3-030-29135-8_9
Fulltext: PDF;
In : 32nd Annual Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada, 2 - 8 Dec 2018, pp.231-264
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