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First Constraints on Nuclear Coupling of Axionlike Particles from the Binary Neutron Star Gravitational Wave Event GW170817

2021· article· en· W3205725292 on OpenAlexafffund
Jun Zhang, Zhenwei Lyu, Junwu Huang, Matthew C. Johnson, Laura Sagunski, Mairi Sakellariadou, Huan Yang

Bibliographic record

VenuePhysical Review Letters · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsUniversity of GuelphYork UniversityPerimeter Institute
FundersScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaDepartament d'Innovació, Universitats i Empresa, Generalitat de CatalunyaEuropean CommissionH2020 European Research CouncilOntario Ministry of Research, Innovation and ScienceGovernment of CanadaInstitut Périmètre de physique théoriqueInnovation, Science and Economic Development Canada
KeywordsPhysicsNeutron starAxionGravitational waveParameter spaceParticle physicsCoupling (piping)Binary numberAstrophysicsNeutronNuclear physicsDark matterStatistics

Abstract

fetched live from OpenAlex

Light axion fields, if they exist, can be sourced by neutron stars due to their coupling to nuclear matter, and play a role in binary neutron star mergers. We report on a search for such axions by analyzing the gravitational waves from the binary neutron star inspiral GW170817. We find no evidence of axions in the sampled parameter space. The null result allows us to impose constraints on axions with masses below 10^{-11} eV by excluding the ones with decay constants ranging from 1.6×10^{16} to 10^{18} GeV at a 3σ confidence level. Our analysis provides the first constraints on axions from neutron star inspirals, and rules out a large region in parameter space that has not been probed by the existing experiments.

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 score0.591
Threshold uncertainty score0.416

Codex and Gemma teacher scores by category

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

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 head0.018
GPT teacher head0.259
Teacher spread0.241 · 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

Citations66
Published2021
Admission routes2
Has abstractyes

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