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Experimental Search for Invisible Dark Matter Axions around <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mn>22</mml:mn><mml:mtext> </mml:mtext><mml:mtext> </mml:mtext><mml:mi mathvariant="normal">μ</mml:mi><mml:mi>eV</mml:mi></mml:mrow></mml:math>

2024· article· en· W4401174372 on OpenAlexaff
Young-Geun Kim, J. H. Jeong, SungWoo Youn, Sungjae Bae, Kiwoong Lee, Arjan F. van Loo, Yasunobu Nakamura, Seonjeong Oh, Taehyeon Seong, S. Uchaikin, Jihn E. Kim, Yannis K. Semertzidis

Bibliographic record

VenuePhysical Review Letters · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsKootenay Association for Science & Technology
FundersJapan Society for the Promotion of ScienceInstitute for Basic Science
KeywordsPhysicsAlgorithmComputer science

Abstract

fetched live from OpenAlex

The axion has emerged as the most attractive solution to two fundamental questions in modern physics related to the charge-parity invariance in strong interactions and the invisible matter component of our Universe. Over the past decade, there have been many theoretical efforts to constrain the axion mass based on various cosmological assumptions. Interestingly, different approaches from independent groups produce good overlap between 20 and 30 μeV. We performed an experimental search to probe the presence of dark matter axions within this particular mass region. The experiment utilized a multicell cavity haloscope embedded in a 12 T magnetic field to seek for microwave signals induced by the axion-photon coupling. The results ruled out the KSVZ axions as dark matter over a mass range between 21.86 and 22.00 μeV at a 90% confidence level. This represents a sensitive experimental search guided by specific theoretical predictions.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.018
GPT teacher head0.269
Teacher spread0.252 · 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

Citations30
Published2024
Admission routes1
Has abstractyes

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