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The Tracking Machine Learning challenge : Accuracy phase
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...
| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Lenguaje: | eng |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1007/978-3-030-29135-8_9 http://cds.cern.ch/record/2672222 |
| _version_ | 1780962451168165888 |
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| author | Amrouche, Sabrina Basara, Laurent Calafiura, Paolo Estrade, Victor Farrell, Steven Ferreira, Diogo R. Finnie, Liam Finnie, Nicole Germain, Cécile Gligorov, Vladimir Vava Golling, Tobias Gorbunov, Sergey Gray, Heather Guyon, Isabelle Hushchyn, Mikhail Innocente, Vincenzo Kiehn, Moritz Moyse, Edward Puget, Jean-Francois Reina, Yuval Rousseau, David Salzburger, Andreas Ustyuzhanin, Andrey Vlimant, Jean-Roch Wind, Johan Sokrates Xylouris, Trian Yilmaz, Yetkin |
| author_facet | Amrouche, Sabrina Basara, Laurent Calafiura, Paolo Estrade, Victor Farrell, Steven Ferreira, Diogo R. Finnie, Liam Finnie, Nicole Germain, Cécile Gligorov, Vladimir Vava Golling, Tobias Gorbunov, Sergey Gray, Heather Guyon, Isabelle Hushchyn, Mikhail Innocente, Vincenzo Kiehn, Moritz Moyse, Edward Puget, Jean-Francois Reina, Yuval Rousseau, David Salzburger, Andreas Ustyuzhanin, Andrey Vlimant, Jean-Roch Wind, Johan Sokrates Xylouris, Trian Yilmaz, Yetkin |
| author_sort | Amrouche, Sabrina |
| collection | CERN |
| description | 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). Its results were discussed at the competition session at the Neural Information Processing Systems conference (NeurIPS 2018). Given 100.000 points, the participants had to connect them into about 10.000 arcs of circles, following the trajectory of particles issued from very high energy proton collisions. The competition was difficult with a dozen front-runners well ahead of a pack. The single competition score is shown to be accurate and effective in selecting the best algorithms from the domain point of view. The competition has exposed a diversity of approaches, with various roles for Machine Learning, a number of which are discussed in the document |
| id | cern-2672222 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2020 |
| record_format | invenio |
| spelling | cern-26722222022-11-02T04:05:06Zdoi:10.1007/978-3-030-29135-8_9http://cds.cern.ch/record/2672222engAmrouche, SabrinaBasara, LaurentCalafiura, PaoloEstrade, VictorFarrell, StevenFerreira, Diogo R.Finnie, LiamFinnie, NicoleGermain, CécileGligorov, Vladimir VavaGolling, TobiasGorbunov, SergeyGray, HeatherGuyon, IsabelleHushchyn, MikhailInnocente, VincenzoKiehn, MoritzMoyse, EdwardPuget, Jean-FrancoisReina, YuvalRousseau, DavidSalzburger, AndreasUstyuzhanin, AndreyVlimant, Jean-RochWind, Johan SokratesXylouris, TrianYilmaz, YetkinThe Tracking Machine Learning challenge : Accuracy phasephysics.data-anOther Fields of Physicshep-exParticle Physics - ExperimentThis 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). Its results were discussed at the competition session at the Neural Information Processing Systems conference (NeurIPS 2018). Given 100.000 points, the participants had to connect them into about 10.000 arcs of circles, following the trajectory of particles issued from very high energy proton collisions. The competition was difficult with a dozen front-runners well ahead of a pack. The single competition score is shown to be accurate and effective in selecting the best algorithms from the domain point of view. The competition has exposed a diversity of approaches, with various roles for Machine Learning, a number of which are discussed in the documentarXiv:1904.06778oai:cds.cern.ch:26722222020 |
| spellingShingle | physics.data-an Other Fields of Physics hep-ex Particle Physics - Experiment Amrouche, Sabrina Basara, Laurent Calafiura, Paolo Estrade, Victor Farrell, Steven Ferreira, Diogo R. Finnie, Liam Finnie, Nicole Germain, Cécile Gligorov, Vladimir Vava Golling, Tobias Gorbunov, Sergey Gray, Heather Guyon, Isabelle Hushchyn, Mikhail Innocente, Vincenzo Kiehn, Moritz Moyse, Edward Puget, Jean-Francois Reina, Yuval Rousseau, David Salzburger, Andreas Ustyuzhanin, Andrey Vlimant, Jean-Roch Wind, Johan Sokrates Xylouris, Trian Yilmaz, Yetkin The Tracking Machine Learning challenge : Accuracy phase |
| title | The Tracking Machine Learning challenge : Accuracy phase |
| title_full | The Tracking Machine Learning challenge : Accuracy phase |
| title_fullStr | The Tracking Machine Learning challenge : Accuracy phase |
| title_full_unstemmed | The Tracking Machine Learning challenge : Accuracy phase |
| title_short | The Tracking Machine Learning challenge : Accuracy phase |
| title_sort | tracking machine learning challenge : accuracy phase |
| topic | physics.data-an Other Fields of Physics hep-ex Particle Physics - Experiment |
| url | https://dx.doi.org/10.1007/978-3-030-29135-8_9 http://cds.cern.ch/record/2672222 |
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