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Record W2931184910 · doi:10.1103/physrevd.99.112007

Measurement of neutron production in atmospheric neutrino interactions at the Sudbury Neutrino Observatory

2019· article· en· W2931184910 on OpenAlexafffundabout
B. Aharmim, S. N. Ahmed, A. E. Anthony, N. Barros, A. Bellerive, B. Beltrán, M. Bergevin, S. D. Biller, R. Bonventre, K. Boudjemline, M. G. Boulay, B. Cai, J. Caravaca Rodríguez, Y. D. Chan, D. Chauhan, M. Chen, B. T. Cleveland, G. A. Cox, X. Dai, Huiqiu Deng, F. Descamps, J. A. Detwiler, P. J. Doe, G. Doucas, P.‐L. Drouin, M. Dunford, S. R. Elliott, H. C. Evans, G. T. Ewan, J. Farine, H. Fergani, F. Fleurot, R. Ford, J. A. Formaggio, N. Gagnon, K. Gilje, J. TM. Goon, K. Graham, E. Guillian, S. Habib, R. L. Hahn, A. L. Hallin, E. D. Hallman, P. J. Harvey, R. Hazama, W. J. Heintzelman, J. Heise, R. L. Helmer, A. Hime, C. Howard, M. Huang, P. Jagam, B. Jamieson, N.A. Jelley, M. Jerkins, K. Keeter, J. R. Klein, L. L. Kormos, M. Kos, A. Krüger, C. Kraus, C. B. Krauss, T. Kutter, Christopher C. M. Kyba, Benjamin Land, R. Lange, J. Law, I. Lawson, K. T. Lesko, J. R. Leslie, I. Levine, J. C. Loach, R. MacLellan, Steve Majerus, H.‐B. Mak, J. Maneira, R. D. Martin, A. Mastbaum, N. McCauley, A. B. McDonald, S. McGee, M. L. Miller, B. Monreal, J. Monroe, B. G. Nickel, A. J. Noble, H. M. O’Keeffe, N. S. Oblath, C. Okada, R. W. Ollerhead, G. D. Orebi Gann, S. M. Oser, R. A. Ott, S. J. M. Peeters, A. W. P. Poon, G. Prior, S. D. Reitzner, K. Rielage, B.C. Robertson, R. G. H. Robertson, M. H. Schwendener, J. A. Secrest, S. R. Seibert, O. Simard, D. Sinclair, J. Singh, P. Skensved, M. Smiley, T. Sonley, L. C. Stonehill, G. Tešić, N. Tolich, T. Tsui, R. Van Berg, B. A. VanDevender, C.J. Virtue, B. L. Wall, D. Waller, H. Wan Chan Tseung, D. L. Wark, J. Wendland, N. West, J. F. Wilkerson, J. R. Wilson, T. Winchester, A. Wright, M. Yeh, F. Zhang, Κ. Zuber

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsUniversity of British ColumbiaUniversity of GuelphLaurentian UniversityUniversity of AlbertaSnolabQueen's UniversityTRIUMFCarleton University
FundersFundação para a Ciência e a TecnologiaNuclear PhysicsAlfred P. Sloan FoundationNatural Sciences and Engineering Research Council of CanadaNational Nuclear Security AdministrationAtomic Energy of Canada LimitedCanada Research ChairsScience and Technology Facilities CouncilStrongNational Research Council CanadaIndustry CanadaCanada Foundation for InnovationU.S. Department of EnergyNational Science Foundation
KeywordsPhysicsNeutrinoNeutronNuclear physicsMonte Carlo methodSolar neutrinoCharged currentParticle physicsNeutrino oscillation

Abstract

fetched live from OpenAlex

Neutron production in giga electron volt--scale neutrino interactions is a poorly studied process. We have measured the neutron multiplicities in atmospheric neutrino interactions in the Sudbury Neutrino Observatory experiment and compared them to the prediction of a Monte Carlo simulation using genie and a minimally modified version of geant4. We analyzed 837 days of exposure corresponding to Phase I, using pure heavy water, and Phase II, using a mixture of Cl in heavy water. Neutrons produced in atmospheric neutrino interactions were identified with an efficiency of 15.3% and 44.3%, for Phases I and II respectively. The neutron production is measured as a function of the visible energy of the neutrino interaction and, for charged current quasielastic interaction candidates, also as a function of the neutrino energy. This study is also performed by classifying the complete sample into two pairs of event categories: charged current quasielastic and non charged current quasielastic, and ${\ensuremath{\nu}}_{\ensuremath{\mu}}$ and ${\ensuremath{\nu}}_{e}$. Results show good overall agreement between data and Monte Carlo for both phases, with some small tension with a statistical significance below $2\ensuremath{\sigma}$ for some intermediate energies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.035
GPT teacher head0.402
Teacher spread0.366 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations7
Published2019
Admission routes3
Has abstractyes

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