Dark matter phase-in: producing feebly-interacting particles after a first-order phase transition
Bibliographic record
Abstract
A bstract The freeze-in mechanism describes the out-of-equilibrium production of dark matter (DM) particles via feeble couplings or non-renormalisable interactions with large suppression scales. In the latter case, predictions suffer from a strong sensitivity to the initial conditions of the universe, such as the details of reheating. In this work, we investigate how this sensitivity is altered in the presence of a cosmological first-order phase transition. We show that freeze-in via non-renormalisable interactions is not always dominated by the highest temperatures of the Standard Model (SM) thermal bath, but instead may be governed by the period immediately after the phase transition, during which the decaying scalar field transfers its energy density to the SM radiation. We refer to this alternative production regime as DM phase-in . Using numerical and approximate analytical solutions of the relevant Boltzmann equations, we determine the conditions under which phase-in or conventional freeze-in production dominates the final DM abundance in terms of the type of interaction between the DM and SM particles, the amount of supercooling before and the evolution of the scalar field after the phase transition. In the phase-in regime, the DM abundance is correlated with the peak frequency of the gravitational wave signal associated with the phase transition, opening up new observational possibilities.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".