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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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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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Machine Learning in High Energy Physics Community White Paper / Albertsson, Kim (Lulea U.) ; Altoe, Piero (NVIDIA, Santa Clara) ; Anderson, Dustin (Caltech) ; Anderson, John ; Andrews, Michael (Carnegie Mellon U.) ; Araque Espinosa, Juan Pedro (LIP, Lisbon) ; Aurisano, Adam (Cincinnati U.) ; Basara, Laurent (INFN, Padua ; Padua U.) ; Bevan, Adrian (University Coll. London) ; Bhimji, Wahid (LBL, Berkeley) et al.
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas for machine learning in particle physics. [...]
arXiv:1807.02876; FERMILAB-PUB-18-318-CD-DI-PPD.- 2018-10-18 - 27 p. - Published in : J. Phys.: Conf. Ser. 1085 (2018) 022008 Fulltext: 1807.02876 - PDF; Albertsson_2018_J._Phys.__Conf._Ser._1085_022008 - PDF; fulltext1681439 - PDF; fermilab-pub-18-318-cd-di-ppd - PDF; Fulltext from Publisher: PDF; External link: Fermilab Library Server (fulltext available)
In : 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Seattle, WA, USA, 21 - 25 Aug 2017, pp.022008

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Performance of the Electromagnetic Calorimeter of AMS-02 on the International Space Station ans measurement of the positronic fraction in the 1.5 – 350 GeV energy range / Basara, Laurent
The AMS-02 experiment is a particle detector installed on the International Space Station (ISS) since May 2011, which measures the characteristics of the cosmic rays to bring answers to the problematics risen by the astroparticle physics since a few decades, in particular the study of dark matter an [...]
343 p.

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