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Cosmological Tests of General Relativity with Future Tomographic Surveys

2009· article· en· W1989691804 on OpenAlexafffund
Gong‐Bo Zhao, Levon Pogosian, Alessandra Silvestri, Joel Zylberberg

Bibliographic record

VenuePhysical Review Letters · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsPhysicsGeneral relativityDark energyTheoretical physicsCurvatureDark matterGravitationTests of general relativityTheory of relativityCosmologyWeak gravitational lensingNeutrinoCOSMIC cancer databaseStatistical physicsClassical mechanicsNumerical relativityAstrophysicsParticle physicsRedshift

Abstract

fetched live from OpenAlex

Future weak lensing surveys will map the evolution of matter perturbations and gravitational potentials, yielding a new test of general relativity on cosmic scales. They will probe the relations between matter overdensities, local curvature, and the Newtonian potential. These relations can be modified in alternative gravity theories or by the effects of massive neutrinos or exotic dark energy fluids. We introduce two functions of time and scale which account for any such modifications in the linear regime. We use a principal component analysis to find the eigenmodes of these functions that cosmological data will constrain. The number of constrained modes gives a model-independent forecast of how many parameters describing deviations from general relativity could be constrained, along with w(z). The modes' scale and time dependence tell us which theoretical models will be better tested.

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)0.009
metaresearch head score (Gemma)0.046
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 score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.046
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.003
Scholarly communication0.0020.006
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.010
GPT teacher head0.277
Teacher spread0.267 · 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

Citations108
Published2009
Admission routes2
Has abstractyes

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