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Record W4386268555 · doi:10.1088/1475-7516/2023/12/017

Probing early modification of gravity with Planck, ACT and SPT

2023· article· en· W4386268555 on OpenAlexaff
Guillermo Franco Abellán, Matteo Braglia, M. Ballardini, F. Finelli⋆, Vivian Poulin

Bibliographic record

VenueJournal of Cosmology and Astroparticle Physics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsPhysicsPlanckDark energyCosmic microwave backgroundBaryon acoustic oscillationsHubble's lawPlanck massGravitationAstrophysicsTheoretical physicsMathematical physicsCosmologyClassical mechanicsQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract We consider a model of early modified gravity (EMG) that was recently proposed as a candidate to resolve the Hubble tension. The model consists of a scalar field σ with a nonminimal coupling (NMC) to the Ricci curvature of the form F ( σ ) = M pl 2 + ξσ 2 and an effective mass induced by a quartic potential V ( σ ) = λσ 4 /4. We present the first analyses of the EMG model in light of the latest ACT DR4 and SPT-3G data in combination with full Planck data, and find a ≳ 2σ preference for a non-zero EMG contribution from a combination of primary CMB data alone, mostly driven by ACT-DR4 data. This is different from popular `Early Dark Energy' models, which are detected only when the high-ℓ information from Planck temperature is removed. We find that the NMC plays a key role in controlling the evolution of density perturbations that is favored by the data over the minimally coupled case. Including measurements of supernovae luminosity distance from Pantheon+, baryonic acoustic oscillations and growth factor from BOSS, and CMB lensing of Planck leaves the preference unaffected. In the EMG model, the tension with S H 0 ES is alleviated from ∼ 6 σ to ∼ 3 σ . Further adding S H 0 ES data raises the detection of the EMG model above 5 σ .

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.259
Teacher spread0.244 · 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

Citations27
Published2023
Admission routes1
Has abstractyes

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