Unified Dark Matter, Dark Energy and Baryogenesis via a "Cosmological Wetting Transition''
Bibliographic record
Abstract
In a recent publication \cite{us}, a cosmological scenario featuring a scalar field, $φ$, that is a source for Dark Matter and Dark Energy has been proposed. In this paper, a concrete realization of that scenario is presented. As in many models of scalar-field driven Dark Energy, the effective Lagrangian of the field $φ$ contains a potential proportional to $e^{-φ/f}$. This potential is modulated in such a way that, in the absence of other matter fields, it has a local minimum at a small value of $φ$. Fluctuations of $φ$ around this minimum give rise to a gas of dark-matter particles. The field $φ$ is coupled to another scalar field $χ$ in such a way that the minimum in the effective potential of $φ$ disappears when, after a continuous phase transition accompanied by spontaneous symmetry breaking, $χ$ develops a non-vanishing expectation value. This triggers slow growth of a homogeneous component of $φ$ accompanied by the emergence of Dark Energy, a phenomenon analogous to the "wetting transition" in statistical mechanics. Inside regions of the Universe where the pressure is small and the energy density is large enough to stall expansion, in particular around galaxies and galaxy clusters, the phase transition in the state of $χ$ does not take place, and a gas of cold dark-matter particles persists. The evolution of $φ$ at very early times may tune the rate of baryogenesis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".