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Strong Constraints on Neutrino Nonstandard Interactions from TeV-Scale <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>ν</mml:mi><mml:mi>μ</mml:mi></mml:msub></mml:math> Disappearance at IceCube

2022· article· lv· W4283712171 on OpenAlexafffund
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Dembinski, Abhishek Desai, P. Desiati, K. D. de Vries, G. de Wasseige, M. de With, T. DeYoung, Alejandro Diaz, J. C. Díaz–Vélez, Markus Dittmer, H. Dujmovic, M. Dunkman, M. A. DuVernois, Emily Dvorak, T. Ehrhardt, P. Eller, R. Engel, H. Erpenbeck, John Evans, P. A. Evenson, Kwok Lung Fan, A. R. Fazely, Anatoli Fedynitch, Nora Feigl, Sebastian Fiedlschuster, Aaron Fienberg, K. Filimonov, C. Finley, Leander Fischer, D. B. Fox, A. Franckowiak, Edward Friedman, Alexander Fritz, Philipp Fürst, T. K. Gaisser, J. Gallagher, Erik Ganster, Alfonso Garcia, S. Garrappa, L. Gerhardt, A. Ghadimi, Christian Gläser, Theo Glauch, T. Glüsenkamp, J. G. Gonzalez, Sreetama Goswami, D. Grant, T. Grégoire, Spencer Griswold, C. Günther, Pascal Gutjahr, Christian Haack, A. Hallgren, R. Halliday, L. Halve, F. Halzen, M. Ha Minh, K. Hanson, J. Hardin, A. A. Harnisch, A. Haungs, D. Hebecker, K. Helbing, F. Henningsen, Emma C. Hettinger, S. Hickford, J. Hignight, Colton Hill, G. C. Hill, K. D. Hoffman, R. Hoffmann, K. Hoshina, F. Huang, M. Huber, Thomas Huber, K. Hultqvist, M. Hünnefeld, R. Hussain, Karolin Hymon, S. In, N. Iovine, A. Ishihara, M. Jansson, G. S. Japaridze, Minjin Jeong, Miaochen Jin, B. J. P. Jones, Donghwa Kang, Woosik Kang, X. Kang, A. Kappes, D. Kappesser, Leonora Kardum, T. Karg, M. Karl, A. Karle, U. Katz, M. Kauer, Moritz Kellermann, J. L. Kelley, A. Kheirandish, K. Kin, T. Kintscher, J. Kiryluk, Ramesh Koirala, H. Kolanoski, T. Kontrimas, L. Köpke, Claudio Kopper, S. Kopper, D. J. Koskinen, Paras Koundal, M. Kovacevich, M. Kowalski, T. Kozynets, Emma Kun, N. Kurahashi, Neha Navnitkumar Lad, Cristina Lagunas Gualda, J. L. Lanfranchi, M. J. Larson, Frederik Hermann Lauber, Jeffrey Lazar, J. W. Lee, Agnieszka Leszczyńska, Yijia Li, Massimiliano Lincetto, Qinrui Liu, M. Liubarska, Elisa Lohfink, L. Lu, Francesco Lucarelli, A. Ludwig, William Luszczak, W. Y., J. Madsen, K. B. M. Mahn, Y. Makino, Sarah Mancina, Ioana Codrina Mariş, Ivan Martínez-Soler, R. Maruyama, S. McCarthy, Thomas McElroy, F. McNally, J. V. Mead, K. Meagher, Sarah Mechbal, A. Medina, Maximilian Meier, S. Meighen-Berger, Jessie Micallef, D. Mockler, T. Montaruli, R. W. Moore, R. Morse, Marjon Moulai, Richard Naab, R. Nagai, U. Naumann, J. Necker, Le Viet Nguyen, Hans Niederhausen, M. U. Nisa, S. C. Nowicki, A. Obertacke Pollmann, M. Oehler, Bob Oeyen, A. Olivas, Erin O’Sullivan, Hershal Pandya, D. V. Pankova, N. Park, G. K. Parker, E. N. Paudel, L. Paul, C. Pérez de los Heros, L. Peters, Josh Peterson, S. Philippen, Sarah Pieper, Martin Pittermann, A. Pizzuto, M. Plum, Yuriy Popovych, Alessio Porcelli, M. Prado Rodriguez, P. Buford Price, B. Pries, G. T. Przybylski, Christoph Raab, J. Rack-Helleis, A. Raissi, M. Rameez, K. Rawlins, I. C. Rea, Z. Rechav, Abdul Rehman, Patrick Reichherzer, R. Reimann, Giovanni Renzi, E. Resconi, S. Reusch, W. Rhode, M. Richman, Benedikt Riedel, E. J. Roberts, S. Robertson, G. Roellinghoff, Martin Rongen, C. Rott, T. Ruhe, D. 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Bibliographic record

VenuePhysical Review Letters · 2022
Typearticle
Languagelv
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 PhysicsChiba UniversityInstitute for Global Prominent Research, Chiba UniversityRWTH Aachen UniversityKnut och Alice Wallenbergs StiftelseVillum FondenNational Research Foundation of KoreaMarsden FundBundesministerium für Bildung und ForschungSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science FoundationBelgian Federal Science Policy OfficeDeutsche ForschungsgemeinschaftUniversity of OxfordFonds Wetenschappelijk OnderzoekMarquette UniversityCanada Foundation for InnovationDeutsches Elektronen-SynchrotronNational Energy Research Scientific Computing CenterAustralian Research CouncilWestern Canada Research GridFonds De La Recherche Scientifique - FNRSPolarforskningssekretariatetUniversity of MarylandWisconsin Alumni Research FoundationCompute CanadaVetenskapsrådetU.S. Department of EnergyUniversity of Wisconsin-MadisonNational Research FoundationMichigan State UniversityCarlsbergfondet
KeywordsNeutrinoPhysicsZenithMuonParticle physicsConfidence intervalScale (ratio)Neutrino oscillationNuclear physicsStatisticsMathematicsQuantum mechanicsOptics

Abstract

fetched live from OpenAlex

We report a search for nonstandard neutrino interactions (NSI) using eight years of TeV-scale atmospheric muon neutrino data from the IceCube Neutrino Observatory. By reconstructing incident energies and zenith angles for atmospheric neutrino events, this analysis presents unified confidence intervals for the NSI parameter ε_{μτ}. The best-fit value is consistent with no NSI at a p value of 25.2%. With a 90% confidence interval of -0.0041≤ε_{μτ}≤0.0031 along the real axis and similar strength in the complex plane, this result is the strongest constraint on any NSI parameter from any oscillation channel to date.

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 imitation

Not 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)0.003
metaresearch head score (Gemma)0.012
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.014
GPT teacher head0.251
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine 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

Citations19
Published2022
Admission routes2
Has abstractyes

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