Machine learning based event reconstruction for the MUonE experiment
Fuente:
arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866914669652869120 |
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| author | Zdybal, Milosz Kucharczyk, Marcin Wolter, Marcin |
| author_facet | Zdybal, Milosz Kucharczyk, Marcin Wolter, Marcin |
| contents | A proof-of-concept solution based on the machine learning techniques has been implemented and tested within the MUonE experiment designed to search for New Physics in the sector of anomalous magnetic moment of a muon. The results of the DNN based algorithm are comparable to the classical reconstruction, reducing enormously the execution time for the pattern recognition phase. The present implementation meets the conditions of classical reconstruction, providing an advantageous basis for further studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02913 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning based event reconstruction for the MUonE experiment Zdybal, Milosz Kucharczyk, Marcin Wolter, Marcin High Energy Physics - Experiment Instrumentation and Detectors A proof-of-concept solution based on the machine learning techniques has been implemented and tested within the MUonE experiment designed to search for New Physics in the sector of anomalous magnetic moment of a muon. The results of the DNN based algorithm are comparable to the classical reconstruction, reducing enormously the execution time for the pattern recognition phase. The present implementation meets the conditions of classical reconstruction, providing an advantageous basis for further studies. |
| title | Machine learning based event reconstruction for the MUonE experiment |
| topic | High Energy Physics - Experiment Instrumentation and Detectors |
| url | https://arxiv.org/abs/2402.02913 |