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Record W6908453130 · doi:10.25916/sut.26218508

RCSLenS: Testing gravitational physics through the cross-correlation of weak lensing and large-scale structure

2024· article· en· W6908453130 on OpenAlexaboutno aff

Bibliographic record

VenueSwinburne Research Bank (Swinburne University of Technology) · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsDark energyWeak gravitational lensingGravitational lensCosmologyStrong gravitational lensingRedshiftGravitational lensing formalismSouth Pole TelescopeGalaxy

Abstract

fetched live from OpenAlex

The unknown nature of ‘dark energy’ motivates continued cosmological tests of large-scale gravitational physics. We present a new consistency check based on the relative amplitude of non-relativistic galaxy peculiar motions, measured via redshift-space distortion, and the relativistic deflection of light by those same galaxies traced by galaxy-galaxy lensing. We take advantage of the latest generation of deep, overlapping imaging and spectroscopic data sets, combining the Red Cluster Sequence Lensing Survey, the Canada-France-Hawaii Telescope Lensing Survey, the WiggleZ Dark Energy Survey and the Baryon Oscillation Spectroscopic Survey. We quantify the results using the ‘gravitational slip’ statistic EG, which we estimate as 0.48 ± 0.10 at z = 0.32 and 0.30 ± 0.07 at z = 0.57, the latter constituting the highest redshift at which this quantity has been determined. These measurements are consistent with the predictions of General Relativity, for a perturbed Friedmann-Robertson-Walker metric in a Universe dominated by a cosmological constant, which are EG = 0.41 and 0.36 at these respective redshifts. The combination of redshift-space distortion and gravitational lensing data from current and future galaxy surveys will offer increasingly stringent tests of fundamental 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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.276
Teacher spread0.256 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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