Machine learning in experimental neutrino physics
Neutrino physics has entered into the era of precision measurements. Over the last two decades, significant efforts have been made to measure precise parameters of the PMNS matrix, which describes neutrino oscillation phenomena. The next generation neutrino experiment will prioritize measuring lepto...
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| Vydáno v: | The European physical journal. ST, Special topics Ročník 233; číslo 15-16; s. 2687 - 2698 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2024
Springer Nature B.V |
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| ISSN: | 1951-6355, 1951-6401 |
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| Abstract | Neutrino physics has entered into the era of precision measurements. Over the last two decades, significant efforts have been made to measure precise parameters of the PMNS matrix, which describes neutrino oscillation phenomena. The next generation neutrino experiment will prioritize measuring leptonic CP-violation, potentially revealing the matter–antimatter asymmetry of the universe. Technological advancements will enable faster and more precise measurements. This article describes how neutrino experiments, will utilize machine learning techniques to identify and reconstruct different neutrino event topology in detectors. This approach promises unprecedented measurements of neutrino oscillation parameters. |
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| AbstractList | Neutrino physics has entered into the era of precision measurements. Over the last two decades, significant efforts have been made to measure precise parameters of the PMNS matrix, which describes neutrino oscillation phenomena. The next generation neutrino experiment will prioritize measuring leptonic CP-violation, potentially revealing the matter–antimatter asymmetry of the universe. Technological advancements will enable faster and more precise measurements. This article describes how neutrino experiments, will utilize machine learning techniques to identify and reconstruct different neutrino event topology in detectors. This approach promises unprecedented measurements of neutrino oscillation parameters. |
| Author | Poonthottathil, N. |
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| Cites_doi | 10.1016/j.nima.2005.09.022 10.1088/1748-0221/11/09/P09001 10.1103/PhysRevLett.124.051103 10.1088/1748-0221/16/07/P07041 10.3389/fdata.2022.978857 10.1109/TNS.2021.3085428 10.1109/ICMLA.2018.00064 10.2172/1415814 10.1126/science.aat1378 10.1126/science.1242856 10.25358/openscience-8530 10.1088/1748-0221/13/04/P04009 10.1162/neco.1989.1.4.541 10.1109/CVPR.2015.7298594 10.22323/1.301.1057 10.3389/frai.2021.649917 10.1088/1748-0221/16/10/C10011 |
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| Copyright | The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. |
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Sci. doi: 10.1109/TNS.2021.3085428 – ident: 1280_CR8 doi: 10.1109/ICMLA.2018.00064 – ident: 1280_CR1 doi: 10.2172/1415814 – volume: 361 start-page: 1378 issue: 6398 year: 2018 ident: 1280_CR11 publication-title: Science doi: 10.1126/science.aat1378 – volume: 342 start-page: 1242856 year: 2013 ident: 1280_CR10 publication-title: Science doi: 10.1126/science.1242856 – ident: 1280_CR7 – volume: 251 start-page: 1242856 year: 2021 ident: 1280_CR6 publication-title: EPJ Web Conf. doi: 10.1126/science.1242856 – ident: 1280_CR15 doi: 10.25358/openscience-8530 – ident: 1280_CR16 doi: 10.1088/1748-0221/13/04/P04009 – volume: 1 start-page: 541 issue: 4 year: 1989 ident: 1280_CR4 publication-title: Neural Comput. doi: 10.1162/neco.1989.1.4.541 – ident: 1280_CR5 doi: 10.1109/CVPR.2015.7298594 – ident: 1280_CR14 doi: 10.22323/1.301.1057 – ident: 1280_CR18 doi: 10.1126/science.1242856 – volume: 4 start-page: 370 issue: 555 year: 2021 ident: 1280_CR9 publication-title: Front. Artif. Intell. doi: 10.3389/frai.2021.649917 – volume: 16 start-page: 10011 issue: 10 year: 2021 ident: 1280_CR17 publication-title: JINST doi: 10.1088/1748-0221/16/10/C10011 |
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