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Record W4404024622 · doi:10.3847/1538-4357/ad7d90

Cross-correlating Dark Sirens and Galaxies: Constraints on H <sub>0</sub> from GWTC-3 of LIGO–Virgo–KAGRA

2024· article· en· W4404024622 on OpenAlexafffund
Suvodip Mukherjee, Alex Krolewski, B. D. Wandelt, Joseph Silk

Bibliographic record

VenueThe Astrophysical Journal · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsUniversity of WaterlooPerimeter Institute
FundersScience and Technology Facilities CouncilInstitut Périmètre de physique théoriqueCentre National de la Recherche ScientifiqueNational Science FoundationGovernment of CanadaMinistry of Colleges and Universities
KeywordsPhysicsGalaxyAstrophysicsLIGOGravitational wave

Abstract

fetched live from OpenAlex

Abstract We apply the cross-correlation technique to infer the Hubble constant (H 0) of the Universe using gravitational-wave (GW) sources without electromagnetic counterparts (dark sirens) from the third GW Transient Catalog (GWTC-3) and the photometric galaxy surveys 2MPZ and WISE-SuperCOSMOS, and combine these with the bright siren measurement of H 0 from GW170817. The posterior on H 0 with only dark sirens is uninformative due to the small number of well-localized GW sources. Using the eight well-localized dark sirens and the binary neutron star GW170817 with electromagnetic counterpart, we obtain a value of the Hubble constant H 0 = 75.4 − 6 + 11 km s−1 Mpc−1 (median and 68.3% equal-tailed interval) after marginalizing over the matter density and the GW bias parameters. This measurement is mostly driven by the bright siren measurement, and any constraint from dark sirens is not statistically significant. In the future, with more well-localized GW events, the constraints on expansion history will improve.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.306
Teacher spread0.295 · 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 designObservational
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

Citations32
Published2024
Admission routes2
Has abstractyes

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