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Record W4221141709 · doi:10.48550/arxiv.2203.16402

A Unified Cosmological Dark Sector from a Bose-Einstein Condensate

2022· preprint· en· W4221141709 on OpenAlexfundno aff
Saurya Das, Sourav Sur

Bibliographic record

VenuearXiv (Cornell University) · 2022
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Delhi
KeywordsPhysicsDark energyScalar field dark matterDark fluidDark matterBosonCold dark matterWarm dark matterHot dark matterLambda-CDM modelQuantumParticle physicsCosmologyAstrophysicsTheoretical physicsQuantum mechanics

Abstract

fetched live from OpenAlex

We examine the viability of cosmological solution(s) describing a unified picture of the dark side of the universe from a Bose-Einstein condensate (BEC) of light bosons. The energy density of the BEC, together with its quantum potential, can indeed account for such a unification, in the sense that the (dust-like) cold dark matter and the dark energy components emerge from the same source. In particular, the bulk of the dark energy can be attributed to the quantum potential, in the quantum corrected Raychaudhuri-Friedmann equation, when the `macroscopic' BEC wave-function is taken to be such that the corresponding probability density is construed as the energy density of the dusty fluid. However, there arises a purely quantum mechanical back-reaction effect, of even the visible baryons, on the effective dark energy and dark matter contents, which crucially determines the mass of the BEC. We determine the constraint on such a back-reaction, and hence on the BEC mass, from physical considerations, as well as estimate the same using recent observational data.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.057
GPT teacher head0.200
Teacher spread0.143 · 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 designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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