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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2021
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2105.01160 |
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| _version_ | 1866916938141138944 |
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| author | Amrouche, Sabrina Basara, Laurent Calafiura, Paolo Emeliyanov, Dmitry Estrade, Victor Farrell, Steven Germain, Cécile Gligorov, Vladimir Vava Golling, Tobias Gorbunov, Sergey Gray, Heather Guyon, Isabelle Hushchyn, Mikhail Innocente, Vincenzo Kiehn, Moritz Kunze, Marcel Moyse, Edward Rousseau, David Salzburger, Andreas Ustyuzhanin, Andrey Vlimant, Jean-Roch |
| author_facet | Amrouche, Sabrina Basara, Laurent Calafiura, Paolo Emeliyanov, Dmitry Estrade, Victor Farrell, Steven Germain, Cécile Gligorov, Vladimir Vava Golling, Tobias Gorbunov, Sergey Gray, Heather Guyon, Isabelle Hushchyn, Mikhail Innocente, Vincenzo Kiehn, Moritz Kunze, Marcel Moyse, Edward Rousseau, David Salzburger, Andreas Ustyuzhanin, Andrey Vlimant, Jean-Roch |
| contents | 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. created at the Large Hadron Collider (LHC): given O($10^5$) points, the participants had to connect them into O($10^4$) individual groups that represent the particle trajectories which are approximated helical. While in the first phase only the accuracy mattered, the goal of this second phase was a compromise between the accuracy and the speed of inference. Both were measured on the Codalab platform where the participants had to upload their software. The best three participants had solutions with good accuracy and speed an order of magnitude faster than the state of the art when the challenge was designed. Although the core algorithms were less diverse than in the first phase, a diversity of techniques have been used and are described in this paper. The performance of the algorithms are analysed in depth and lessons derived. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2105_01160 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | The Tracking Machine Learning challenge : Throughput phase Amrouche, Sabrina Basara, Laurent Calafiura, Paolo Emeliyanov, Dmitry Estrade, Victor Farrell, Steven Germain, Cécile Gligorov, Vladimir Vava Golling, Tobias Gorbunov, Sergey Gray, Heather Guyon, Isabelle Hushchyn, Mikhail Innocente, Vincenzo Kiehn, Moritz Kunze, Marcel Moyse, Edward Rousseau, David Salzburger, Andreas Ustyuzhanin, Andrey Vlimant, Jean-Roch Machine Learning High Energy Physics - Experiment 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. created at the Large Hadron Collider (LHC): given O($10^5$) points, the participants had to connect them into O($10^4$) individual groups that represent the particle trajectories which are approximated helical. While in the first phase only the accuracy mattered, the goal of this second phase was a compromise between the accuracy and the speed of inference. Both were measured on the Codalab platform where the participants had to upload their software. The best three participants had solutions with good accuracy and speed an order of magnitude faster than the state of the art when the challenge was designed. Although the core algorithms were less diverse than in the first phase, a diversity of techniques have been used and are described in this paper. The performance of the algorithms are analysed in depth and lessons derived. |
| title | The Tracking Machine Learning challenge : Throughput phase |
| topic | Machine Learning High Energy Physics - Experiment |
| url | https://arxiv.org/abs/2105.01160 |