MétaCan
Menu
← Back to cohort

New Method to Determine the Hubble Parameter from Cosmological Energy-Density Measurements

2025· article· en· W4408364087 on OpenAlexafffund
Alex Krolewski, Will J. Percival, Alex Woodfinden

Bibliographic record

VenuePhysical Review Letters · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsPerimeter InstituteUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaCanadian Space AgencyMinistry of Colleges and UniversitiesInnovation, Science and Economic Development CanadaCanadian Institute for Theoretical Astrophysics
KeywordsPhysicsBaryonCosmic microwave backgroundOmegaHubble's lawRedshiftBig Bang nucleosynthesisSupernovaBaryon acoustic oscillationsParticle physicsAstrophysicsGalaxyCosmologyNucleosynthesisQuantum mechanics

Abstract

fetched live from OpenAlex

We introduce a new method for measuring the Hubble parameter from low-redshift large-scale observations that is independent of the comoving sound horizon. The method uses the baryon-to-photon ratio determined by the primordial deuterium abundance, together with big bang nucleosynthesis calculations and the present-day cosmic microwave background (CMB) temperature, to determine the physical baryon density ${\mathrm{\ensuremath{\Omega}}}_{b}{h}^{2}$. The baryon fraction ${\mathrm{\ensuremath{\Omega}}}_{b}/{\mathrm{\ensuremath{\Omega}}}_{m}$ is measured using the relative amplitude of the baryonic signature in galaxy clustering measured by the Baryon Oscillation Spectroscopic Survey, scaling the physical baryon density to the physical matter density. The physical density ${\mathrm{\ensuremath{\Omega}}}_{m}{h}^{2}$ is then compared with the geometrical density ${\mathrm{\ensuremath{\Omega}}}_{m}$ from Alcock-Paczynski measurements from baryon acoustic oscillations (BAO) and voids to give ${H}_{0}$. Including type Ia supernovae and uncalibrated BAO, we measure ${H}_{0}={67.1}_{\ensuremath{-}5.3}^{+6.3}\text{ }\text{ }\mathrm{km}\text{ }{\mathrm{s}}^{\ensuremath{-}1}\text{ }{\mathrm{Mpc}}^{\ensuremath{-}1}$. We find similar results when varying analysis choices, such as measuring the baryon signature from the reconstructed correlation function or excluding supernovae or voids. This measurement is currently consistent with both the distance-ladder and CMB ${H}_{0}$ determinations, but near-future large-scale structure surveys will obtain $3\ifmmode\times\else\texttimes\fi{}$ to $4\ifmmode\times\else\texttimes\fi{}$ tighter constraints.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.003

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 head0.036
GPT teacher head0.340
Teacher spread0.305 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations11
Published2025
Admission routes2
Has abstractyes

Explore more

Same venuePhysical Review Letters→Same topicCosmology and Gravitation Theories→French-language works237,207→