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Track reconstruction at LHC as a collaborative data challenge use case with RAMP
/ Amrouche, Sabrina (Geneva U.) ; Braun, Nils (KIT, Karlsruhe) ; Calafiura, Paolo (LBL, Berkeley) ; Farrell, Steven (LBL, Berkeley) ; Gemmler, Jochen (KIT, Karlsruhe) ; Germain, Cécile (Orsay, LAL ; LRI, Paris 11) ; Gligorov, Vladimir Vava (Paris U., VI-VII) ; Golling, Tobias (Geneva U.) ; Gray, Heather (LBL, Berkeley) ; Guyon, Isabelle (LRI, Paris 11) et al.
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. [...]
2017 - 12 p.
- Published in : EPJ Web Conf. 150 (2017) 00015
In : Connecting The Dots / Intelligent Trackers 2017, Orsay, France, 6 - 9 Mar 2017, pp.00015
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2.
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The TrackML high-energy physics tracking challenge on Kaggle
/ Kiehn, Moritz (Geneva U.) ; Amrouche, Sabrina (Geneva U.) ; Calafiura, Paolo (LBL, Berkeley) ; Estrade, Victor (LRI, Paris 11) ; Farrell, Steven (LBL, Berkeley) ; Germain, Cécile (LRI, Paris 11) ; Gligorov, Vava (Paris U., VI-VII) ; Golling, Tobias (Geneva U.) ; Gray, Heather (LBL, Berkeley) ; Guyon, Isabelle (INRIA, Saclay ; Unlisted, US) et al.
The High-Luminosity LHC (HL-LHC) is expected to reach unprecedented collision intensities, which in turn will greatly increase the complexity of tracking within the event reconstruction. To reach out to computer science specialists, a tracking machine learning challenge (TrackML) was set up on Kaggle 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. [...]
2019 - 7 p.
- Published in : EPJ Web Conf. 214 (2019) 06037
Fulltext from publisher: PDF;
In : 23rd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2018, Sofia, Bulgaria, 9 - 13 Jul 2018, pp.06037
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3.
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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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4.
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Tracking at LHC as a collaborative data challenge use case with RAMP
/ Amrouche, Sabrina (University of Geneva, Switzerland) ; Braun, Nils (Karlsruhe Institute of Technology, Germany) ; Calafiura, Paolo (Lawrence Berkeley National Laboratory, CA, USA) ; Farrell, Steven (Lawrence Berkeley National Laboratory, CA, USA) ; Gammler, Jochen (Karlsruhe Institute of Technology, Germany) ; Germain, Cécile (LAL and LRI, Orsay, France) ; Gligorov, Vladimir Vava (LPNHE, Paris, France) ; Golling, Tobias (University of Geneva, Switzerland) ; Grasland, Hadrien (LAL, Univ. Paris-Sud, CNRS/IN2P3, Université Paris-Saclay, Orsay, France) ; Gray, Heather (Lawrence Berkeley National Laboratory, CA, USA) et al.
Charged particle tracking has been a major component of data-processing in high-energy physics experiments such as the Large Hadron Collider (LHC), and is fore- seen to become more and more challenging. There are many ways to perform the tracking task; a collaborative platform, RAMP, has been set up so that developers can create algo- rithms to solve a simplified 2D tracking problems. [...]
AIDA-2020-CONF-2017-002.-
Geneva : CERN, 2017
- Published in :
Fulltext: PDF;
In : Connecting The Dots / Intelligent Trackers 2017, Orsay, France, 6 - 9 Mar 2017
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5.
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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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6.
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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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7.
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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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8.
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Deep learning for inferring cause of data anomalies
/ Azzolini, V. (MIT) ; Borisyak, M. (Higher Sch. of Economics, Moscow ; Yandex Sch. Data Anal., Moscow) ; Cerminara, G. (CERN) ; Derkach, D. (Higher Sch. of Economics, Moscow ; Yandex Sch. Data Anal., Moscow) ; Franzoni, G. (CERN) ; De Guio, F. (Texas Tech.) ; Koval, O. (Skoltech ; Yandex Sch. Data Anal., Moscow) ; Pierini, M. (CERN) ; Pol, A. (U. Paris-Saclay) ; Ratnikov, F. (Higher Sch. of Economics, Moscow ; Yandex Sch. Data Anal., Moscow) et al.
Daily operation of a large-scale experiment is a resource consuming task, particularly from perspectives of routine data quality monitoring. Typically, data comes from different sub-detectors and the global quality of data depends on the combinatorial performance of each of them. [...]
arXiv:1711.07051.-
2018-10-18 - 6 p.
- Published in : J. Phys.: Conf. Ser. 1085 (2018) 042015
Fulltext: PDF; Fulltext from Publisher: PDF; Preprint: PDF;
In : 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Seattle, WA, USA, 21 - 25 Aug 2017, pp.042015
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9.
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Machine Learning based Global Particle Identification Algorithms at the LHCb Experiment
/ Derkach, Denis (Higher Sch. of Economics, Moscow) ; Hushchyn, Mikhail (Higher Sch. of Economics, Moscow ; Yandex Sch. Data Anal., Moscow) ; Kazeev, Nikita (Higher Sch. of Economics, Moscow ; Yandex Sch. Data Anal., Moscow ; Rome U.)
/LHCb Collaboration
One of the most important aspects of data processing at flavor physics experiments is the particle identification (PID) algorithm. In LHCb,several different sub-detector systems provide PID information: the Ring Imaging Cherenkov detectors, the hadronic and electromagnetic calorimeters, and the muon chambers. [...]
EDP Sciences, 2019 - 4 p.
- Published in : EPJ Web Conf. 214 (2019) 06011
Fulltext: PDF;
In : 23rd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2018, Sofia, Bulgaria, 9 - 13 Jul 2018, pp.06011
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10.
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Machine-Learning-based global particle-identification algorithms at the LHCb experiment
/ Derkach, Denis (Higher Sch. of Economics, Moscow ; Yandex Sch. Data Anal., Moscow) ; Hushchyn, Mikhail (Higher Sch. of Economics, Moscow ; Yandex Sch. Data Anal., Moscow ; Moscow, MIPT) ; Likhomanenko, Tatiana (Yandex Sch. Data Anal., Moscow ; Kurchatov Inst., Moscow) ; Rogozhnikov, Alex (Yandex Sch. Data Anal., Moscow) ; Kazeev, Nikita (Higher Sch. of Economics, Moscow ; Yandex Sch. Data Anal., Moscow) ; Chekalina, Victoria (Higher Sch. of Economics, Moscow ; Yandex Sch. Data Anal., Moscow) ; Neychev, Radoslav (Higher Sch. of Economics, Moscow ; Yandex Sch. Data Anal., Moscow) ; Kirillov, Stanislav (Yandex Sch. Data Anal., Moscow) ; Ratnikov, Fedor (Higher Sch. of Economics, Moscow ; Yandex Sch. Data Anal., Moscow)
/LHCb
One of the most important aspects of data analysis at the LHC experiments is the particle identification (PID). In LHCb, several different sub-detectors provide PID information: two Ring Imaging Cherenkov (RICH) detectors, the hadronic and electromagnetic calorimeters, and the muon chambers. [...]
2018 - 5 p.
- Published in : J. Phys.: Conf. Ser. 1085 (2018) 042038
Fulltext: PDF;
In : 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Seattle, WA, USA, 21 - 25 Aug 2017, pp.042038
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