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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| Published in: | The European physical journal. ST, Special topics Vol. 233; no. 15-16; pp. 2687 - 2698 |
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| Format: | Journal Article |
| Language: | English |
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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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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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| SubjectTerms | Algorithms Antimatter Atomic Charged particles Classical and Continuum Physics Condensed Matter Physics Cosmic rays CP violation Experiments Machine learning Materials Science Measurement Science and Instrumentation Modern Machine Learning and Particle Physics: An In-Depth Review Molecular Neural networks Neutrinos Optical and Plasma Physics Parameters Physics Physics and Astronomy Review Sensors Topology |
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