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Record W4386367361 · doi:10.48550/arxiv.2308.16183

Late Time Modification of Structure Growth and the S8 Tension

2023· preprint· en· W4386367361 on OpenAlexafffund
Mengxiang Lin, Bhuvnesh Jain, Marco Raveri, Eric J. Baxter, C. Chang, M. Gatti, S. Lee, J. Muir

Bibliographic record

VenuearXiv (Cornell University) · 2023
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsPerimeter Institute
FundersGovernment of CanadaNational Energy Research Scientific Computing CenterMinistry of Colleges and UniversitiesUniversity of PennsylvaniaOffice of ScienceNational Aeronautics and Space AdministrationU.S. Department of Energy
KeywordsPhysicsCosmic microwave backgroundRedshiftAstrophysicsGalaxyWeak gravitational lensingLambdaDark energySigmaStructure formationPhenomenological modelCosmologyAstronomyOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

The $S_8$ tension between low-redshift galaxy surveys and the primary CMB signals a possible breakdown of the $Λ$CDM model. Recently differing results have been obtained using low-redshift galaxy surveys and the higher redshifts probed by CMB lensing, motivating a possible time-dependent modification to the growth of structure. We investigate a simple phenomenological model in which the growth of structure deviates from the $Λ$CDM prediction at late times, in particular as a simple function of the dark energy density. Fitting to galaxy lensing, CMB lensing, BAO, and Supernovae datasets, we find significant evidence - 2.5 - 3$σ$, depending on analysis choices - for a non-zero value of the parameter quantifying a deviation from $Λ$CDM. The preferred model, which has a slower growth of structure below $z\sim 1$, improves the joint fit to the data over $Λ$CDM. While the overall fit is improved, there is weak evidence for galaxy and CMB lensing favoring different changes in the growth of structure.

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)0.009
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.046
GPT teacher head0.196
Teacher spread0.151 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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