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Record W3104408426

Unified superfluid dark sector

2019· article· en· W3104408426 on OpenAlexaff

Bibliographic record

VenueMPG.PuRe (Max Planck Society) · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicCosmology and Gravitation Theories
Canadian institutionsMcGill University
Fundersnot available
KeywordsPhysicsSuperfluidityDark energyDark matterDark fluidModified Newtonian dynamicsAstrophysicsCosmologyQuantum mechanicsGalaxy formation and evolutionGalaxyGalaxy rotation curve
DOInot available

Abstract

fetched live from OpenAlex

We present a novel theory of a unified dark sector, where late-time cosmic acceleration emerges from the dark matter superfluid framework. The system is described by a superfluid mixture consisting of two distinguishable states with a small energy gap, such as the ground state and an excited state of dark matter. Given their contact in the superfluid, interaction between those states can happen, converting one state into the other. This long range interaction within the superfluid couples the two superfluid phonon species through a cosine potential motivated by Josephson/Rabi interactions. As a consequence of this potential, a new dynamics of late-time accelerated expansion emerges in this system, without the need of dark energy, coming from a universe containing only this two-state DM superfluid. Because the superfluid species are non-relativistic, their sound speeds remain suitably small throughout the evolution. We calculate the expansion history and growth of linear perturbations, and compare the results to ΛCDM cosmology. For the fiducial parameters studied here, the predicted expansion and growth function are close to those of ΛCDM, but the difference in the predicted growth rate is significant at late times. The present theory nicely complements the recent proposal of dark matter superfluidity to explain the empirical success of MOdified Newtonian Dynamics (MOND) on galactic scales, thus offering a unified framework for dark matter, dark energy, and MOND phenomenology.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.998

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)0.0030.001

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 head0.006
GPT teacher head0.213
Teacher spread0.207 · 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.

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

Citations44
Published2019
Admission routes1
Has abstractyes

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