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First Measurement of High-Energy Reactor Antineutrinos at Daya Bay

2022· article· en· W4285802803 on OpenAlexaff
Fengpeng An, Weidong Bai, A. B. Balantekin, M. Bishai, S. Blyth, G. F. Cao, Jun Cao, J. F. Chang, Y. Chang, H S Chen, H. Chen, S M Chen, Y. Chen, Y X Chen, Jie Cheng, Zhaokan Cheng, J. J. Cherwinka, M. C. Chu, J. P. Cummings, Olivia Dalager, F. S. Deng, Ying Ding, M. Diwan, Tadeáš Dohnal, Dmitry Dolzhikov, J. Dove, D. A. Dwyer, J. P. Gallo, M. Gonchar, G. Gong, Haipeng Gong, W. Gu, J. Y. Guo, Lei Guo, Xin-Heng Guo, Yuhang Guo, Ziyi Guo, R. Hackenburg, S. Hans, M. He, K. M. Heeger, Y. K. Heng, Y. K. Hor, Y. Hsiung, Beibei Hu, Jun Hu, T. Hu, Z. Hu, Han‐Xiong Huang, Jihong Huang, X. T. Huang, Y. B. Huang, Patrick Huber, D. E. Jaffe, K. L. Jen, X. L. Ji, Xiangpan Ji, R. A. Johnson, D. Jones, Li-Wei Kang, S. H. Kettell, S. Kohn, M. Krämer, T. J. Langford, J. Lee, J. H. C. Lee, R. T. Lei, R. Leitner, J. K. C. Leung, F Li, H L Li, Jinjing Li, Qiang Li, R H Li, S. Li, S. Li, W D Li, X N Li, X. Li, Y F Li, Zongwang Li, Han Liang, C.-J. Lin, Guey-Lin Lin, S. Lin, J. J. Ling, J. M. Link, L. Littenberg, B. R. Littlejohn, J C Liu, Jianglai Liu, Jin Liu, C. Lu, H. Q. Lu, X.-G. Lu, B Z, X B, X Y, Y Q, R. C. Mandujano, C. Marshall, Kirk T. McDonald, R. D. McKeown, Yue Meng, J. Napolitano, D. Naumov, E. Naumova, T. M. T. Nguyen, J. P. Ochoa‐Ricoux, A. Olshevskiy, H.-R. Pan, J. Park, S. Patton, J. C. Peng, C. S. J. Pun, F. Z. Qi, M. Qi, X. Qian, N. Raper, Jie Ren, C. Morales Reveco, R. Rosero, B. Roskovec, Xichao Ruan, H. Steiner, Jian Sun, Tomáš Tměj, Konstantin Treskov, W-H Tse, C. E. Tull, B. Viren, V. Vorobel, C H Wang, Jun Wang, M. Wang, N Y Wang, Ruiguang Wang, W Wang, X. Wang, Y. Wang, Y F Wang, Z. Wang, Z M Wang, H. Wei, Lianghong Wei, Liangjian Wen, K. Whisnant, C.G. White, H. L. H. Wong, E. Worcester, Diru Wu, Q. Wu, W. Wu, D. M. Xia, Z. Q. Xie, Z. Z. Xing, Huaiyu Xu, Jilei Xu, T. Xu, T. Xue, Changgen Yang, L. Yang, Yifan Yang, H. F. Yao, M. Ye, M. Yeh, Ben Young, H. Z. Yu, Zeyuan Yu, B. B. Yue, Vitalii Zavadskyi, S. Zeng, Yuda Zeng, Liang Zhan, C. Zhang, F Y Zhang, H H Zhang, Ji‐Zong Zhang, Jiawen Zhang, Qingmin Zhang, Sude Zhang, X T Zhang, Yibo Zhang, Y X Zhang, Y Y Zhang, Z J Zhang, Z. P. Zhang, Z Y Zhang, J. Zhao, R. Z. Zhao, L. Zhou, H.L. Zhuang, J. H. Zou

Bibliographic record

VenuePhysical Review Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNeutrino Physics Research
Canadian institutionsInstitute of Particle Physics
FundersComisión Nacional de Investigación Científica y TecnológicaResearch Grants Council, University Grants CommitteeMinisterstvo Školství, Mládeže a TělovýchovyChinese Academy of SciencesGovernment of Guangdong ProvinceNational Natural Science Foundation of ChinaChina RailwayNational Science and Technology Major ProjectUniverzita Karlova v PrazeCAS Center for Excellence in Particle PhysicsMinistry of EducationU.S. Department of EnergyScience, Technology and Innovation Commission of Shenzhen MunicipalityNational Science Foundation
KeywordsPhysicsNuclear physicsNeutrinoFlux (metallurgy)IsotopeEnergy spectrumEnergy (signal processing)Research reactorNeutron

Abstract

fetched live from OpenAlex

This Letter reports the first measurement of high-energy reactor antineutrinos at Daya Bay, with nearly 9000 inverse beta decay candidates in the prompt energy region of 8-12 MeV observed over 1958 days of data collection. A multivariate analysis is used to separate 2500 signal events from background statistically. The hypothesis of no reactor antineutrinos with neutrino energy above 10 MeV is rejected with a significance of 6.2 standard deviations. A 29% antineutrino flux deficit in the prompt energy region of 8-11 MeV is observed compared to a recent model prediction. We provide the unfolded antineutrino spectrum above 7 MeV as a data-based reference for other experiments. This result provides the first direct observation of the production of antineutrinos from several high-Q_{β} isotopes in commercial reactors.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.940

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.281
Teacher spread0.253 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations25
Published2022
Admission routes1
Has abstractyes

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