Measurement of Atmospheric Neutrino Oscillation Parameters Using Convolutional Neural Networks with 9.3 Years of Data in IceCube DeepCore

R. Abbasi; Jakob Beise; Olga Botner; Alan Coleman; Christian Glaser; Thorsten Glüsenkamp; Allan Hallgren; Nils Heyer; Erin O'Sullivan; Carlos Pérez de los Heros; Axel Pontén; Martin Ravn; Nora Valtonen-Mattila; M. Zimmerman; IceCube Collaboration; Matheus Hostert

doi:10.1103/PhysRevLett.134.091801

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Measurement of Atmospheric Neutrino Oscillation Parameters Using Convolutional Neural Networks with 9.3 Years of Data in IceCube DeepCore

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Measurement of Atmospheric Neutrino Oscillation Parameters Using Convolutional Neural Networks with 9.3 Years of Data in IceCube DeepCore

R. Abbasi, Jakob Beise, Olga Botner, Alan Coleman, Christian Glaser, Thorsten Glüsenkamp, Allan Hallgren, Nils Heyer, Erin O'Sullivan, Carlos Pérez de los Heros, …

Physical review letters, Vol.134(9), p.091801

03/07/2025

DOI: 10.1103/PhysRevLett.134.091801

PMID: 40131048

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Abstract

The DeepCore subdetector of the IceCube Neutrino Observatory provides access to neutrinos with energies above approximately 5 GeV. Data taken between 2012 and 2021 (3387 days) are utilized for an atmospheric nu(mu) disappearance analysis that studied 150 257 neutrino-candidate events with reconstructed energies between 5 and 100 GeV. An advanced reconstruction based on a convolutional neural network is applied, providing increased signal efficiency and background suppression, resulting in a measurement with both significantly increased statistics compared to previous DeepCore oscillation results and high neutrino purity. For the normal neutrino mass ordering, the atmospheric neutrino oscillation parameters and their 1 sigma errors are measured to be Delta m(32)(2) = 2.40(-0.04)(+0.05) x 10(-3) eV(2) and sin(2)theta(23) = 0.54(-0.03)(+0.04). The results are the most precise to date using atmospheric neutrinos, and are compatible with measurements from other neutrino detectors including long-baseline accelerator experiments.

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Title: Subtitle: Measurement of Atmospheric Neutrino Oscillation Parameters Using Convolutional Neural Networks with 9.3 Years of Data in IceCube DeepCore
Creators: R. Abbasi - Loyola University Chicago
Jakob Beise - Uppsala University
Olga Botner - Uppsala University
Alan Coleman - Uppsala University
Christian Glaser - Uppsala University
Thorsten Glüsenkamp - Friedrich-Alexander-Universität Erlangen-Nürnberg
Allan Hallgren - Uppsala University
Nils Heyer - Uppsala University
Erin O'Sullivan - Högenergifysik
Carlos Pérez de los Heros - Högenergifysik
Axel Pontén - Uppsala University
Martin Ravn - Uppsala University
Nora Valtonen-Mattila - Uppsala University
M. Zimmerman - University of Wisconsin–Madison
IceCube Collaboration
Matheus Hostert (Contributor) - Physics and Astronomy
Resource Type: Journal article
Publication Details: Physical review letters, Vol.134(9), p.091801
DOI: 10.1103/PhysRevLett.134.091801
PMID: 40131048
NLM abbreviation: Phys Rev Lett
ISSN: 0031-9007
eISSN: 1079-7114
Language: English
Date published: 03/07/2025
Academic Unit: Physics and Astronomy
Record Identifier: 9984945913002771

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