Evolutionary Algorithms for Tracking Algorithm Parameter Optimization
The reconstruction of charged particle trajectories, known as tracking, is one of the most complex and CPU consuming parts of event processing in high energy particle physics experiments. The most widely used and best performing tracking algorithms require significant geometry-specific tuning of the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2021-01-01
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| Series: | EPJ Web of Conferences |
| Online Access: | https://www.epj-conferences.org/articles/epjconf/pdf/2021/05/epjconf_chep2021_03071.pdf |
| _version_ | 1831524474445365248 |
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| author | Chatain Peter Garg Rocky Tompkins Lauren |
| author_facet | Chatain Peter Garg Rocky Tompkins Lauren |
| author_sort | Chatain Peter |
| collection | DOAJ |
| description | The reconstruction of charged particle trajectories, known as tracking, is one of the most complex and CPU consuming parts of event processing in high energy particle physics experiments. The most widely used and best performing tracking algorithms require significant geometry-specific tuning of the algorithm parameters to achieve best results. In this paper, we demonstrate the usage of machine learning techniques, particularly evolutionary algorithms, to find high performing configurations for the first step of tracking, called track seeding. We use a track seeding algorithm from the software framework A Common Tracking Software (ACTS). ACTS aims to provide an experimentindependent and framework-independent tracking software designed for modern computing architectures. We show that our optimization algorithms find highly performing configurations in ACTS without hand-tuning. These techniques can be applied to other reconstruction tasks, improving performance and reducing the need for laborious hand-tuning of parameters. |
| first_indexed | 2024-12-16T11:02:53Z |
| format | Article |
| id | doaj.art-3a3e0c1f4cd04aa582e49fef90d98610 |
| institution | Directory Open Access Journal |
| issn | 2100-014X |
| language | English |
| last_indexed | 2024-12-16T11:02:53Z |
| publishDate | 2021-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | EPJ Web of Conferences |
| spelling | doaj.art-3a3e0c1f4cd04aa582e49fef90d986102022-12-21T22:33:57ZengEDP SciencesEPJ Web of Conferences2100-014X2021-01-012510307110.1051/epjconf/202125103071epjconf_chep2021_03071Evolutionary Algorithms for Tracking Algorithm Parameter OptimizationChatain Peter0Garg Rocky1Tompkins Lauren2Stanford UniversityStanford UniversityStanford UniversityThe reconstruction of charged particle trajectories, known as tracking, is one of the most complex and CPU consuming parts of event processing in high energy particle physics experiments. The most widely used and best performing tracking algorithms require significant geometry-specific tuning of the algorithm parameters to achieve best results. In this paper, we demonstrate the usage of machine learning techniques, particularly evolutionary algorithms, to find high performing configurations for the first step of tracking, called track seeding. We use a track seeding algorithm from the software framework A Common Tracking Software (ACTS). ACTS aims to provide an experimentindependent and framework-independent tracking software designed for modern computing architectures. We show that our optimization algorithms find highly performing configurations in ACTS without hand-tuning. These techniques can be applied to other reconstruction tasks, improving performance and reducing the need for laborious hand-tuning of parameters.https://www.epj-conferences.org/articles/epjconf/pdf/2021/05/epjconf_chep2021_03071.pdf |
| spellingShingle | Chatain Peter Garg Rocky Tompkins Lauren Evolutionary Algorithms for Tracking Algorithm Parameter Optimization EPJ Web of Conferences |
| title | Evolutionary Algorithms for Tracking Algorithm Parameter Optimization |
| title_full | Evolutionary Algorithms for Tracking Algorithm Parameter Optimization |
| title_fullStr | Evolutionary Algorithms for Tracking Algorithm Parameter Optimization |
| title_full_unstemmed | Evolutionary Algorithms for Tracking Algorithm Parameter Optimization |
| title_short | Evolutionary Algorithms for Tracking Algorithm Parameter Optimization |
| title_sort | evolutionary algorithms for tracking algorithm parameter optimization |
| url | https://www.epj-conferences.org/articles/epjconf/pdf/2021/05/epjconf_chep2021_03071.pdf |
| work_keys_str_mv | AT chatainpeter evolutionaryalgorithmsfortrackingalgorithmparameteroptimization AT gargrocky evolutionaryalgorithmsfortrackingalgorithmparameteroptimization AT tompkinslauren evolutionaryalgorithmsfortrackingalgorithmparameteroptimization |