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Record W2738189124 · doi:10.1103/physrevd.96.103529

Constraining interacting dark energy with CMB and BAO future surveys

2017· article· en· W2738189124 on OpenAlexafffund
Larissa Santos, Wen Zhao, Elisa G. M. Ferreira, Jerome Quintin

Bibliographic record

VenuePhysical review. D/Physical review. D. · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsMcGill University
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoChinese Academy of SciencesNatural Sciences and Engineering Research Council of CanadaUniversity of Science and Technology of ChinaNational Natural Science Foundation of China
KeywordsCosmic microwave backgroundDark energyPhysicsBaryon acoustic oscillationsDark matterTheoretical physicsContext (archaeology)Statistical physicsAstrophysicsParticle physicsCosmologyQuantum mechanics

Abstract

fetched live from OpenAlex

In this paper, we perform a forecast analysis to test the capacity of future baryon acoustic oscillation (BAO) and cosmic microwave background (CMB) experiments to constrain phenomenological interacting dark energy models using the Fisher matrix formalism. We consider a Euclid-like experiment, in which BAO measurements are one of the main goals, to constrain the cosmological parameters of alternative cosmological models. Moreover, additional experimental probes can more efficiently provide information on the parameters forecast, justifying also the inclusion in the analysis of a future ground-based CMB experiment mainly designed to measure the polarization signal with high precision. In the interacting dark energy scenario, a coupling between dark matter and dark energy modifies the conservation equations such that the fluid equations for both constituents are conserved as the total energy density of the dark sector. In this context, we consider three phenomenological models that have been deeply investigated in literature over the past years. We find that the combination of both CMB and BAO information can break degeneracies among the dark sector parameters for all three models, although to different extents. We find powerful constraints on, for example, the coupling constant when comparing it with present limits for two of the models, and their future statistical $3\ensuremath{\sigma}$ bounds could potentially exclude the null interaction for the combination of probes that is considered. However, for one of the models, the constraint on the coupling parameter does not improve the present result (achieved using a large combination of surveys), and a larger combination of probes appears to be necessary to eventually claim whether or not interaction is favored in that context.

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)0.003
metaresearch head score (Gemma)0.006
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 score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.405
Teacher spread0.391 · 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

Citations45
Published2017
Admission routes2
Has abstractyes

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