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Measurement of Atmospheric Neutrino Oscillation Parameters Using Convolutional Neural Networks with 9.3 Years of Data in IceCube DeepCore
M. Ackermann, J. Adams, Sanjib Kumar Agarwalla, J. A. Aguilar, M. Ahlers, Jean-Marco Alameddine, N. M. Amin, K. Andeen, G. Anton, C. Argüelles, Y. Ashida, S. Athanasiadou, L. Ausborm, S. N. Axani, X. Bai, A. Balagopal V., M. Baricevic, S. W. Barwick, Simeon Bash, Vedant Basu, R. Bay, J. J. Beatty, J. Becker Tjus, J. Beise, C. Bellenghi, Charlotte Benning, S. BenZvi, D. Berley, E. Bernardini, Dominique Besson, E. Blaufuss, Linda B. Bloom, Summer Blot, F. Bontempo, Caterina Boscolo Meneguolo, S. Böser, O. Botner, J. Böttcher, E. Bourbeau, J. Braun, B. Brinson, J. Brostean-Kaiser, Livia Brusa, Ryan T. Burley, Delaney Butterfield, Michael Campana, I. Caracas, K. Carloni, J. Graciá‐Carpio, J. Krishnamoorthi, Thien Nhan Chau, Z. Chen, D. Chirkin, S. Choi, B. A. Clark, A. Coleman, G. H. Collin, A. Connolly, J. M. Conrad, P. Coppin, Rebecca Corley, P. Correa, D. F. Cowen, P. Dave, C. De Clercq, James DeLaunay, D. Delgado, Sen Deng, Amruta Desai, P. Desiati, K. D. de Vries, G. de Wasseige, T. DeYoung, Alejandro A. Díaz, J. C. Díaz-Vélez, P. Dierichs, Markus Dittmer, A. Domi, Lincoln Draper, H. Dujmovic, K. Dutta, M. A. DuVernois, T. Ehrhardt, Leonhard Eidenschink, A. Eimer, P. Eller, E. Ellinger, Sharif El Mentawi, Dominik Elsässer, R. Engel, H. Erpenbeck, J. Evans, P. A. Evenson, Kwok Lung Fan, Ke Fang, Kareem Ramadan Farrag, A. R. Fazely, A. Fedynitch, N. Feigl, S. Fiedlschuster, C. Finley, L. Fischer, D. B. Fox, A. Franckowiak, Satoshi Fukami, Philipp Fürst, J. Gallagher, Erik Ganster, Alyssa Garcia, M. García, Gaurav Garg, Eliot Genton, L. Gerhardt, A. Ghadimi, C. Girard-Carillo, C. Glaser, T. Glüsenkamp, J. G. Gonzalez, Sreetama Goswami, Alejandra Granados, Darren Grant, S. J. Gray, Oliver Gries, S. Griffin, S. Griswold, Kathrine Mørch Groth, C. Günther, Pascal Gutjahr, C. Ha, Christian Haack, A. Hallgren, L. Halve, F. Halzen, H. Hamdaoui, M. Ha Minh, Michael Handt, K. Hanson, J. Hardin, A. A. Harnisch, P. Hatch, A. Haungs, Jonas Häußler, K. Helbing, Jonas Hellrung, J. Hermannsgabner, Lars Philipp Heuermann, N. Heyer, S. Hickford, A. Hidvegi, J. Hignight, C. Hill, G. C. Hill, K. D. Hoffman, Sam Hori, K. Hoshina, Matheus Hostert, Wenjie Hou, Thomas Huber, K. Hultqvist, M. Hünnefeld, R. Hussain, Karolin Hymon, A. Ishihara, W. Iwakiri, M. Jacquart, Oliver Janik, M. Jansson, G. S. Japaridze, Minjin Jeong, Miaochen Jin, Benjamin P. Jones, N. Kamp, Dong Woo Kang, Woosik Kang, X. Kang, A. Kappes, D. Kappesser, Leonora Kardum, T. Karg, M. Karl, A. Karle, Akanksha Katil, U. Katz, M. Kauer, J. L. Kelley, Manish Khanal, A. Khatee Zathul, A. Kheirandish, J. Kiryluk, S. R. Klein, A. Kochocki, R. Koirala, H. Kolanoski, T. Kontrimas, L. Köpke, C. Kopper, D. J. Koskinen, P. Koundal, M. Kovacevich, M. Kowalski, T. Kozynets, K. Kruiswijk, E. Krupczak, Anil Kumar, E. Kun, N. Kurahashi, N. N. Lad, Cristina Lagunas Gualda, M. Lamoureux, M. J. Larson, Silvia Latseva, F. Lauber, Jeffrey Lazar, J. W. Lee, Kayla Leonard DeHolton, A. Leszczyńska, J. Y. Liao, M. Lincetto, Yantao Liu, M. Liubarska, E. Lohfink, Christina Love, C. J. Lozano Mariscal, L. Lu, F. Lucarelli, W. Luszczak, W. Y., J. Madsen, Else Magnus, K. B. M. Mahn, Y. Makino, Elena Manao, S. Mancina, W. Marie Sainte, I. C. Mariş, S. Márka, Z. Márka, M. Marsee, I. Martinez-Soler, R. Maruyama, Finn Mayhew, Frank McNally, J. V. Mead, K. Meagher, S. Mechbal, A. Medina, Maximilian Meier, Y. Merckx, Lukas Merten, J. Micallef, J. Mitchell, T. Montaruli, R. W. Moore, Yasutsugu Morii, R. Morse, Marjon Moulai, T. Mukherjee, Richard Naab, Ryo Nagai, M. Nakos, U. Naumann, J. Necker, Akshima Negi, Ludwig Neste, M. Neumann, H. Niederhausen, M. U. Nisa, K. Noda, A. Noell, Alexander S. Novikov, A. Obertacke Pollmann, Vivian O'Dell, Bob Oeyen, A. Olivas, R. Orsoe, J. Osborn, Erin O’Sullivan, H. Pandya, N. Park, G. K. Parker, E. N. Paudel, L. Paul, C. Pérez de los Heros, Teresa Pernice, J. Peterson, S. Philippen, A. Pizzuto, M. Plum, Axel Ponten, Y. Popovych, M. Prado Rodriguez, B. Pries, R. Procter-Murphy, G. T. Przybylski, Christoph Raab, J. Rack-Helleis, Martin Langgård Ravn, K. Rawlins, Z. Rechav, Abdul Rehman, P. Reichherzer, E. Resconi, S. Reusch, W. Rhode, B. Riedel, Adam Rifaie, E. J. Roberts, S. Robertson, S. Rodan, G. Roellinghoff, M. Rongen, Aske Rosted, C. Rott, T. Ruhe, L. Ruohan, D. Ryckbosch, I. Safa, J. Saffer, D. Salazar-Gallegos, P. Sampathkumar, A. Sandrock, M. Santander, S. Sarkar, S. Sarkar, J. Savelberg, P. Savina, Patrick Schaile, M. Schaufel, H. Schieler, S. Schindler, B. Schlüter, Felix Schlüter, Nick Schmeisser, Torsten C. Schmidt, J. Schneider, F. G. Schröder, L. Schumacher, S. Sclafani, D. Seckel, Mohammad Ful Hossain Seikh, Minhye Seo, S. Seunarine, P. Sevle Myhr, R. Shah, S. Shefali, N. Shimizu, M. Silva, B. Skrzypek, B. Smithers, R. Snihur, J. Soedingrekso, A. Søgaard, D. Soldin, Philipp Soldin, Giacomo Sommani, C. Spannfellner, G. M. Spiczak, C. Spiering, M. Stamatikos, T. Stanev, T. Stezelberger, T. Stürwald, Thomas Stuttard, G. W. Sullivan, I. Taboada, A. Terliuk, Matthias Thiesmeyer, W. G. Thompson, Jessie Thwaites, S. Tilav, K. Tollefson, C. Tönnis, Simona Toscano, D. Tosi, A. Trettin, R. Turcotte, Jean Pierre Twagirayezu, Martin Unland Elorrieta, A. K. Upadhyay, K. Upshaw, A. Vaidyanathan, N. Valtonen-Mattila, J. Vandenbroucke, N. van Eijndhoven, D. Vannerom, J. van Santen, J. Vara, J. Veitch-Michaelis, M. Venugopal, Matthias Vereecken, S. Verpoest, Doğa Veske, A. Vijai, C. Walck, Andrew Wang, Chris Weaver, Philip Weigel, Andreas Weindl, J. Weldert, Alex Wen, C. Wendt, J. Werthebach, M. Weyrauch, N. Whitehorn, C. H. Wiebusch, D. R. Williams, J. R. Willison, Lucas Witthaus, A. Wolf, M. Wolf, Gerrit Wrede, Xiaolin Xu, J. P. Yanez, Emre Burak Yildizci, S. Yoshida, R. Young, Shaoqing Yu, T. Yuan, Z. Zhang, P. Zhelnin, Perri Zilberman, Melany Zimmerman
Bibliographic record
VenuePhysical Review Letters · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Cosmic Phenomena
Canadian institutionsUniversity of AlbertaQueen's University
FundersOffice of Experimental Program to Stimulate Competitive ResearchJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaPhysics Division, National Center for Theoretical SciencesOffice of Polar ProgramsCollege of Engineering, Michigan State UniversityHelmholtz Alliance for Astroparticle PhysicsAlliance de recherche numérique du CanadaChiba UniversityInstitute for Global Prominent Research, Chiba UniversityRWTH Aachen UniversityKnut och Alice Wallenbergs StiftelseMarsden FundBundesministerium für Bildung und ForschungSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science FoundationBelgian Federal Science Policy OfficeDeutsche ForschungsgemeinschaftDeutsches Elektronen-SynchrotronNational Energy Research Scientific Computing CenterAustralian Research CouncilWestern Canada Research GridFonds De La Recherche Scientifique - FNRSPolarforskningssekretariatetUniversity of MarylandWisconsin Alumni Research FoundationNational Research Foundation of KoreaVillum FondenCompute CanadaCanada Foundation for InnovationMarquette UniversityVetenskapsrådetU.S. Department of EnergyUniversity of Wisconsin-MadisonNvidiaCarlsbergfondetOffice of Advanced CyberinfrastructureEuropean CommissionNational Research FoundationMichigan State UniversityFonds Wetenschappelijk Onderzoek
KeywordsPhysicsNeutrinoParticle physicsNeutrino oscillationOscillation (cell signaling)AlgorithmAnalytical Chemistry (journal)Computer science
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 ν_{μ} 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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.045
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.033
GPT teacher head0.275
Teacher spread0.243 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations16
Published2025
Admission routes2
Has abstractyes