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Record W3000497563 · doi:10.1103/physrevd.100.112005

Cosmogenic neutron production at the Sudbury Neutrino Observatory

2019· article· en· W3000497563 on OpenAlexafffundabout
B. Aharmim, S. N. Ahmed, A. E. Anthony, N. Barros, E. W. Beier, A. Bellerive, B. Beltrán, M. Bergevin, S. D. Biller, R. Bonventre, K. Boudjemline, M. G. Boulay, B. Cai, E. J. Callaghan, J. Caravaca Rodríguez, Y. D. Chan, D. Chauhan, M. Chen, B. T. Cleveland, G. A. Cox, R. Curley, 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, C. Kéfélian, 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, P. Skensved, 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 TecnologiaNational Nuclear Security AdministrationAtomic Energy of Canada LimitedVale Canada LimitedQueen's UniversityCanada Research ChairsScience and Technology Facilities CouncilNational Research Council CanadaStrongNuclear PhysicsNatural Sciences and Engineering Research Council of CanadaUniversity of California BerkeleyAlfred P. Sloan FoundationIndustry CanadaCanada Foundation for InnovationUniversity of CaliforniaU.S. Department of EnergyNational Science Foundation
KeywordsPhysicsNuclear physicsNeutronMuonObservatoryCosmic rayExtrapolationNeutrinoAstrophysics

Abstract

fetched live from OpenAlex

Neutrons produced in nuclear interactions initiated by cosmic-ray muons present an irreducible background to many rare-event searches, even in detectors located deep underground. Models for the production of these neutrons have been tested against previous experimental data, but the extrapolation to deeper sites is not well understood. Here we report results from an analysis of cosmogenically produced neutrons at the Sudbury Neutrino Observatory. A specific set of observables are presented, which can be used to benchmark the validity of geant4 physics models. In addition, the cosmogenic neutron yield, in units of ${10}^{\ensuremath{-}4}\text{ }\text{ }{\mathrm{cm}}^{2}/(\mathrm{g}\ifmmode\cdot\else\textperiodcentered\fi{}\ensuremath{\mu})$, is measured to be $7.28\ifmmode\pm\else\textpm\fi{}0.09{(\mathrm{stat})}_{\ensuremath{-}1.12}^{+1.59}(\mathrm{syst})$ in pure heavy water and $7.30\ifmmode\pm\else\textpm\fi{}0.07{(\mathrm{stat})}_{\ensuremath{-}1.02}^{+1.40}(\mathrm{syst})$ in NaCl-loaded heavy water. These results provide unique insights into this potential background source for experiments at SNOLAB.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.012

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 head0.027
GPT teacher head0.409
Teacher spread0.381 · 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 designNot applicable
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

Citations9
Published2019
Admission routes3
Has abstractyes

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