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Record W2286238120 · doi:10.1103/physrevd.93.012004

First measurement of radioactive isotope production through cosmic-ray muon spallation in Super-Kamiokande IV

2016· article· en· W2286238120 on OpenAlexafffund
Yang Zhang, K. Abe, Y. Haga, Y. Hayato, M. Ikeda, K. Iyogi, J. Kameda, Y. Kishimoto, M. Miura, S. Moriyama, M. Nakahata, Tomoki NAKAJIMA, Y. Nakano, S. Nakayama, A. Orii, H. Sekiya, M. Shiozawa, A. Takeda, T. Tomura, R. A. Wendell, T. J. Irvine, T. Kajita, I. Kametani, K. Kaneyuki, Y. Nishimura, E. Richard, K. Okumura, L. Labarga, P. Fernández, J. Gustafson, C. Kachulis, E. Kearns, J. L. Raaf, J. L. Stone, Lawrence Sulak, S. Berkman, C. Nantais, H. A. Tanaka, S. Tobayama, M. Goldhaber, G. Carminati, N. J. Griskevich, W. R. Kropp, S. Mine, A. Renshaw, M. Smy, H. W. Sobel, Volodymyr Takhistov, P. Weatherly, K. S. Ganezer, B. L. Hartfiel, J. Hill, Nguyễn Thị Hồng, J. Y. Kim, I. T. Lim, A. Himmel, Z. Li, K. Scholberg, C. W. Walter, T. Wongjirad, T. Ishizuka, S. Tasaka, J. S. Jang, J. G. Learned, S. Matsuno, S. N. Smith, M. Friend, T. Hasegawa, Toru Ishida, T. Ishii, T. Kobayashi, T. Nakadaira, K. Nakamura, Y. Oyama, K. Sakashita, T. Sekiguchi, T. Tsukamoto, A. T. Suzuki, Y. Takeuchi, T. Yano, S. Hirota, K. Huang, K. Ieki, T. Kikawa, A. Minamino, T. Nakaya, K. Suzuki, Sentaro Takahashi, Y. Fukuda, K. Choi, Y. Itow, T. Suzuki, P. Mijakowski, K. Frankiewicz, J. Hignight, J. Imber, C. K. Jung, X. Li, J. L. Palomino, M. J. Wilking, C. Yanagisawa, H. Ishino, T. Kayano, A. Kibayashi, Y. Koshio, T. Mori, M. Sakuda, Y. Kuno, R. Tacik, S. B. Kim, H. Okazawa, Y. Choi, K. Nishijima, M. Koshiba, Y. Suda, Y. Totsuka, M. Yokoyama, C. Bronner, M. Hartz, K. Martens, Ll. Marti, Y. Suzuki, M. R. Vagins, J. F. Martin, P. de Perio, A. Konaka, S. Chen, R. J. Wilkes

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsUniversity of ReginaUniversity of TorontoTRIUMFUniversity of British Columbia
FundersNational Natural Science Foundation of ChinaNatural Sciences and Engineering Research Council of CanadaEuropean CommissionNational Research Foundation of KoreaU.S. Department of EnergyJapan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and TechnologyNational Science Foundation
KeywordsPhysicsNuclear physicsSpallationIsotopeMuonNeutrinoCosmic raySuper-KamiokandeSupernovaDecay chainScintillatorNeutronSolar neutrinoRadiochemistryNeutrino oscillationAstrophysicsChemistryDetector

Abstract

fetched live from OpenAlex

Cosmic-ray-muon spallation-induced radioactive isotopes with $\ensuremath{\beta}$ decays are one of the major backgrounds for solar, reactor, and supernova relic neutrino experiments. Unlike in scintillator, production yields for cosmogenic backgrounds in water have not been exclusively measured before, yet they are becoming more and more important in next generation neutrino experiments designed to search for rare signals. We have analyzed the low-energy trigger data collected at Super-Kamiokande IV in order to determine the production rates of $^{12}\mathrm{B}$, $^{12}\mathrm{N}$, $^{16}\mathrm{N}$, $^{11}\mathrm{Be}$, $^{9}\mathrm{Li}$, $^{8}\mathrm{He}$, $^{9}\mathrm{C}$, $^{8}\mathrm{Li}$, $^{8}\mathrm{B}$, and $^{15}\mathrm{C}$. These rates were extracted from fits to time differences between parent muons and subsequent daughter $\ensuremath{\beta}$'s by fixing the known isotope lifetimes. Since $^{9}\mathrm{Li}$ can fake an inverse-beta-decay reaction chain via a $\ensuremath{\beta}+n$ cascade decay, producing an irreducible background with detected energy up to a dozen MeV, a dedicated study is needed for evaluating its impact on future measurements; the application of a neutron tagging technique using correlated triggers was found to improve this $^{9}\mathrm{Li}$ measurement. The measured yields were generally found to be comparable with theoretical calculations, except the cases of the isotopes $^{8}\mathrm{Li}/^{8}\mathrm{B}$ and $^{9}\mathrm{Li}$.

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)
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 score0.180
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.041
GPT teacher head0.420
Teacher spread0.379 · 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

Citations59
Published2016
Admission routes2
Has abstractyes

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