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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Veröffentlicht in:The European physical journal. ST, Special topics Jg. 233; H. 15-16; S. 2687 - 2698
1. Verfasser: Poonthottathil, N.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2024
Springer Nature B.V
Schlagworte:
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
ISSN:1951-6355, 1951-6401
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1951-6355
1951-6401
DOI:10.1140/epjs/s11734-024-01280-6