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Record W4389891893 · doi:10.1103/physrevd.108.112010

Measurement of the multineutron <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mover accent="true"><mml:mi>ν</mml:mi><mml:mo stretchy="false">¯</mml:mo></mml:mover><mml:mi>μ</mml:mi></mml:msub></mml:math> charged current differential cross section at low available energy on hydrocarbon

2023· article· lv· W4389891893 on OpenAlexaff
A. Olivier, T. Cai, S. Akhter, Z. Ahmad Dar, V. Ansari, M. V. Ascencio, M. Sajjad Athar, A. Bashyal, A. Bercellie, M. Betancourt, J. L. Bonilla, A. Bravar, H. S. Budd, G. Cáceres, G. A. Díaz, J. Félix, L. Fields, A. Filkins, R. Fine, A. M. Gago, P. K. Gaur, S. M. Gilligan, R. Gran, E. Granados, D. A. Harris, Ailsa Hart, C. Jena, S. Jena, J. Kleykamp, A. Klustová, M. Kordosky, D. Last, A. Lozano, X.-G. Lu, S. Manly, W. A. Mann, C. Mauger, K. S. McFarland, B. Messerly, Omar Moreno, J. G. Morfín, D. Naples, J. K. Nelson, C. Nguyen, V. Paolone, G. N. Perdue, C. Pernas, Komninos-John Plows, M. A. Ramírez, H. Ray, N. Roy, D. Ruterbories, H. Schellman, C. J. Solano Salinas, M. Sultana, V. S. Syrotenko, E. Valencia, N. H. Vaughan, A. V. Waldron, B. Yaeggy, L. Zazueta

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2023
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsYork University
FundersFermilabFondo Nacional de Desarrollo Científico y TecnológicoComisión Nacional de Investigación Científica y TecnológicaOffice of ScienceConsejo Nacional de Ciencia, Tecnología e Innovación TecnológicaNarodowe Centrum NaukiH2020 Marie Skłodowska-Curie ActionsDirección de Gestión de la Investigación, Universidad de AntofagastaConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorScience and Technology Facilities CouncilUniversity of RochesterU.S. Department of EnergyAgencia Nacional de Investigación y DesarrolloPontificia Universidad Católica del PerúNational Science FoundationImperial College LondonConsejo Nacional de Ciencia y Tecnología
KeywordsNeutrinoNeutronPhysicsNuclear physicsEnergy (signal processing)Computer science

Abstract

fetched live from OpenAlex

Neutron production in antineutrino interactions can lead to bias in energy reconstruction in neutrino oscillation experiments, but these interactions have rarely been studied. MINERvA previously studied neutron production at an average antineutrino energy of $\ensuremath{\sim}3\text{ }\text{ }\mathrm{GeV}$ in 2016 and found deficiencies in leading models. In this paper, the MINERvA 6 GeV average antineutrino energy dataset is shown to have similar disagreements. A measurement of the cross section for an antineutrino to produce two or more neutrons and have low visible energy is presented as an experiment-independent way to explore neutron production modeling. This cross section disagrees with several leading models' predictions. Neutron modeling techniques from nuclear physics are used to quantify neutron detection uncertainties on this result.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.910
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.005

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 head0.026
GPT teacher head0.326
Teacher spread0.299 · 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; both teacher heads agree on what is shown here.

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
Published2023
Admission routes1
Has abstractyes

Explore more

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