Freezing-in a hot bath: resonances, medium effects and phase transitions
Bibliographic record
Abstract
Abstract Relic density calculations of dark matter freezing out from the primordial plasma have reached a high level of sophistication, with several numerical tools readily available that match the observationally required accuracy. Dark matter production via the freeze-in mechanism, on the other hand, is sensitive to much higher temperatures than in the freeze-out case, implying both technical and computational difficulties when aiming for the same level of precision. We revisit the formulation of freeze-in production in a way that facilitates the inclusion of in-medium corrections like plasma effects and the spin statistics of relativistic quantum gases, as well as the temperature dependence of dark matter production rates induced by the electroweak and strong phase transitions, and we discuss in detail the additional complications arising in the presence of s-channel resonances. We illustrate our approach in the context of Higgs portal models, and provide the most accurate calculation to date of the freeze-in abundance of Scalar Singlet dark matter. We explore in particular the case of small reheating temperatures, for which the couplings implied by the freeze-in mechanism may be testable at the LHC. Together with this article we present a major update 6.3 of DarkSUSY with the added capability of performing general freeze-in calculations, including all complications mentioned above.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".