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Record W4417212334 · doi:10.1103/n5hr-zljn

Ultralight boson constraints from gravitational wave observations of spinning binary black holes

2025· article· en· W4417212334 on OpenAlexafffund

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsPerimeter Institute
FundersNatural Sciences and Engineering Research Council of CanadaInnovation, Science and Economic Development CanadaInstitut Périmètre de physique théoriqueAustralian Research CouncilMinistry of Colleges and UniversitiesNational Science Foundation
KeywordsGravitational waveBlack hole (networking)Binary black holeStellar black holeBosonScalar (mathematics)Spin-flipAxionSuperradianceSpinning

Abstract

fetched live from OpenAlex

In the presence of an ultralight scalar or vector boson, a spinning black hole will be spun down through the superradiant instability. We use spin measurements from gravitational wave observations of binary black holes, in particular the heavy binary black hole merger event GW231123, along with the lower-mass GW190517 event, to constrain the existence of ultralight bosons. We disfavor scalars with masses in the range of $[0.55, 11]\times 10^{-13}$ eV and vectors in the range of $[0.11, 18]\times 10^{-13}$ eV, making only a conservative assumption that the black hole lifetimes are greater than $10^5$ years. The lower ends of these ranges, where the exclusion confidence is the highest, were not previously excluded by spin measurements from electromagnetic or gravitational wave observations. We map these constraints to axion and dark photon models with interactions.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.035
GPT teacher head0.460
Teacher spread0.424 · 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 designSimulation or modeling
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

Citations6
Published2025
Admission routes2
Has abstractyes

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