| Title: |
Measurement of Atmospheric Neutrino Oscillation Parameters Using Convolutional Neural Networks with 9.3 Years of Data in IceCube DeepCore |
| Authors: |
Abbasi, R; Ackermann, M; Adams, J; Agarwalla, SK; Aguilar, JA; Ahlers, M; Alameddine, JM; Amin, NM; Andeen, K; Anton, G; Argüelles, C; Ashida, Y; Athanasiadou, S; Ausborm, L; Axani, SN; Bai, X; V., A Balagopal; Baricevic, M; Barwick, SW; Bash, S; Basu, V; Bay, R; Beatty, JJ; Tjus, J Becker; Beise, J; Bellenghi, C; Benning, C; BenZvi, S; Berley, D; Bernardini, E; Besson, DZ; Blaufuss, E; Bloom, L; Blot, S; Bontempo, F; Motzkin, JY Book; Meneguolo, C Boscolo; Böser, S; Botner, O; Böttcher, J; Bourbeau, E; Braun, J; Brinson, B; Brostean-Kaiser, J; Brusa, L; Burley, RT; Butterfield, D; Campana, MA; Caracas, I; Carloni, K; Carpio, J; Chattopadhyay, S; Chau, N; Chen, Z; Chirkin, D; Choi, S; Clark, BA; Coleman, A; Collin, GH; Connolly, A; Conrad, JM; Coppin, P; Corley, R; Correa, P; Cowen, DF; Dave, P; De Clercq, C; DeLaunay, JJ; Delgado, D; Deng, S; Desai, A; Desiati, P; de Vries, KD; de Wasseige, G; DeYoung, T; Diaz, A; Díaz-Vélez, JC; Dierichs, P; Dittmer, M; Domi, A; Draper, L; Dujmovic, H; Dutta, K; DuVernois, MA; Ehrhardt, T; Eidenschink, L; Eimer, A; Eller, P; Ellinger, E; Mentawi, S El; Elsässer, D; Engel, R; Erpenbeck, H; Evans, J; Evenson, PA; Fan, KL; Fang, K; Farrag, K; Fazely, AR; Fedynitch, A |
| Source: |
Physical Review Letters, vol 134, iss 9 |
| Publisher Information: |
eScholarship, University of California |
| Publication Year: |
2025 |
| Collection: |
University of California: eScholarship |
| Subject Terms: |
5106 Nuclear and Plasma Physics (for-2020); 5107 Particle and High Energy Physics (for-2020); 51 Physical Sciences (for-2020); Bioengineering (rcdc); Machine Learning and Artificial Intelligence (rcdc); IceCube Collaboration; 01 Mathematical Sciences (for); 02 Physical Sciences (for); 09 Engineering (for); General Physics (science-metrix); 40 Engineering (for-2020); 49 Mathematical sciences (for-2020) |
| Description: |
The DeepCore subdetector of the IceCube Neutrino Observatory provides access to neutrinos with energies above approximately 5GeV. Data taken between 2012 and 2021 (3387days) are utilized for an atmospheric ν_{μ} disappearance analysis that studied 150 257 neutrino-candidate events with reconstructed energies between 5 and 100GeV. 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 1σ errors are measured to be Δm_{32}^{2}=2.40_{-0.04}^{+0.05}×10^{-3} eV^{2} and sin^{2}θ_{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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
qt9p45899g; https://escholarship.org/uc/item/9p45899g; https://escholarship.org/content/qt9p45899g/qt9p45899g.pdf |
| DOI: |
10.1103/physrevlett.134.091801 |
| Availability: |
https://escholarship.org/uc/item/9p45899g; https://escholarship.org/content/qt9p45899g/qt9p45899g.pdf; https://doi.org/10.1103/physrevlett.134.091801 |
| Rights: |
CC-BY |
| Accession Number: |
edsbas.6141FBC3 |
| Database: |
BASE |