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Record W3110141700 · doi:10.1051/0004-6361/201936724

Velocity correction for Hubble constant measurements from standard sirens

2020· article· en· W3110141700 on OpenAlexfundno aff
Suvodip Mukherjee, Guilhem Lavaux, F. R. Bouchet, Jens Jasche, B. D. Wandelt, S. Nissanke, Florent Leclercq, Kenta Hotokezaka

Bibliographic record

VenueAstronomy and Astrophysics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsnot available
FundersAgence Nationale de la RechercheImperial College LondonGrand Équipement National De Calcul IntensifNederlandse Organisatie voor Wetenschappelijk OnderzoekYork UniversityFlatiron HealthPrinceton UniversitySimons Foundation
KeywordsPhysicsHubble's lawAstrophysicsAstronomyConstant (computer programming)Hubble volumeCosmic distance ladderRadial velocityCosmologyStarsGalaxyDark energy

Abstract

fetched live from OpenAlex

Gravitational wave (GW) sources are an excellent probe of the luminosity distance and offer a novel measure of the Hubble constant, H 0 . This estimation of H 0 from standard sirens requires an accurate estimation of the cosmological redshift of the host galaxy of the GW source after correcting for its peculiar velocity. The absence of an accurate peculiar velocity correction affects both the precision and accuracy of the measurement of H 0 , particularly for nearby sources. Here, we propose a framework to incorporate such a peculiar velocity correction for GW sources. A first implementation of our method to the event GW170817, combined with observations taken with Very Large Baseline Interferometry (VLBI), leads to a revised value of H 0 = 68.3 −4.5 +4.6 km s −1 Mpc −1 . While this revision is minor, it demonstrates that our method makes it possible to obtain unbiased and accurate measurements of H 0 at the precision required for the standard siren cosmology.

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)0.017
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.031
GPT teacher head0.295
Teacher spread0.263 · 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

Citations100
Published2020
Admission routes1
Has abstractyes

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