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Record W4322760318 · doi:10.48550/arxiv.2302.14639

Precision Measurement of the Specific Activity of $^{39}$Ar in Atmospheric Argon with the DEAP-3600 Detector

2023· preprint· en· W4322760318 on OpenAlexafffund
Prabal Adhikari, Rahaf Ajaj, M. Alpízar-Venegas, P. Amaudruz, James Anstey, G. R. Araujo, D. J. Auty, M.N. Baldwin, M. Batygov, B. Beltrán, H. Benmansour, C. E. Bina, J. Bonatt, W. Bonivento, M. G. Boulay, B. Broerman, J. F. Bueno, P. M. Burghardt, A. Butcher, M. Cadeddu, B. Cai, Miguel Cárdenas‐Montes, S. Cavuoti, M. Chen, Y. Chen, S. Choudhary, B. T. Cleveland, J. M. Corning, R. Crampton, D. Cranshaw, S. Daughtery, P. DelGobbo, K. Dering, P. Di Stefano, J. DiGioseffo, G. Dolganov, L. Doria, F. A. Duncan, M. Dunford, E. Ellingwood, A. Erlandson, S. S. Farahani, N. Fatemighomi, G. Fiorillo, A. Flower, R. Ford, R. Gagnon, D. Gallacher, P. García Abia, S. Garg, P. Giampa, A. Giménez-Alcázar, D. Goeldi, V. V. Golovko, P. Gorel, K. Graham, D. R. Grant, A. Grobov, A. L. Hallin, M. Hamstra, P. J. Harvey, S. Haskins, C. Hearns, J. Hu, J. Hucker, T. Hugues, A. Ilyasov, B. Jigmeddorj, C. J. Jillings, A. Joy, O. Kamaev, G. Kaur, A. Kemp, M. Kuźniak, F. La Zia, M. Laí, S. Langrock, B. Lehnert, A. Leonhardt, J. LePage-Bourbonnais, N. Levashko, J. Lidgard, T. Lindner, M. Lissia, James A. Lock, I. Machulin, Peter Majewski, Apexa Maru, James Mason, A. B. McDonald, Thomas McElroy, T. McGinn, Joseph McLaughlin, R. Mehdiyev, C. Mielnichuk, L. Mirasola, J. Monroe, Philippe Nadeau, C. Nantais, C. Ng, A. J. Noble, E. O’Dwyer, G. Oliviéro, C. Ouellet, Sanjoy Kumar Pal, D. Papi, P. Pasuthip, S. J. M. Peeters, M. G. Perry, V. Pesudo, E. Picciau, M.-C. Piro, T. R. Pollmann, F. Rad, E. T. Rand, C. Rethmeier, F. Retière, I. Rodríguez-García, Leszek Roszkowski, J. B. Ruhland, R. Santorelli, F. G. Schuckman, N. Seeburn, S. Seth, V. Shalamova, K. Singhrao, P. Skensved, N.J.T. Smith, B. C. Smith, K. Sobotkiewich, T. Sonley, J. Sosiak, J. Soukup, R. Stainforth, Connor Stone, V. Strickland, M. Stringer, B. Sur, J. Tang, E. Vázquez-Jáuregui, L. M. Veloce, S. Viel, B. Vyas, M. Walczak, J. Walding, M. Ward, S. Westerdale, J. L. Willis, A. Zuñiga-Reyes

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsCanadian Nuclear LaboratoriesLaurentian UniversityUniversity of AlbertaSnolabQueen's UniversityCarleton UniversityTRIUMFArthur B. McDonald-Canadian Astroparticle Physics Research Institute
FundersScience and Technology Facilities CouncilDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoNatural Sciences and Engineering Research Council of CanadaSouth East Physics NetworkAlliance de recherche numérique du CanadaLeibniz-GemeinschaftRussian Science FoundationMinisterio de Ciencia e InnovaciónComunidad de MadridConsejo Nacional de Ciencia y TecnologíaCanada First Research Excellence FundQueen's UniversityEuropean Regional Development FundFundacja na rzecz Nauki PolskiejEuropean CommissionFundación Marcos MoshinskyLeverhulme TrustMinistry of Advanced Education, Government of AlbertaOntario Ministry of Research and InnovationUniversity of Alberta
KeywordsArgonDetectorPhysicsAtmosphere (unit)Atmospheric pressureAnalytical Chemistry (journal)ChemistryOpticsAtomic physicsEnvironmental chemistryMeteorology

Abstract

fetched live from OpenAlex

The specific activity of the beta decay of $^{39}$Ar in atmospheric argon is measured using the DEAP-3600 detector. DEAP-3600, located 2 km underground at SNOLAB, uses a total of (3269 $\pm$ 24) kg of liquid argon distilled from the atmosphere to search for dark matter. This detector with very low background uses pulseshape discrimination to differentiate between nuclear recoils and electron recoils and is well-suited to measure the decay of $^{39}$Ar. With 167 live-days of data, the measured specific activity at the time of atmospheric extraction is [0.964 $\pm$ 0.001 (stat) $\pm$ 0.024 (sys)] Bq/kg$_{\rm atmAr}$ which is consistent with results from other experiments. A cross-check analysis using different event selection criteria provides a consistent 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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.053
GPT teacher head0.171
Teacher spread0.118 · 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 source (direct Gemma or distilled Codex), 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

Citations4
Published2023
Admission routes2
Has abstractyes

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