Implications of the observation of dark matter self-interactions for singlet scalar dark matter
Bibliographic record
Abstract
Evidence for dark matter self-interactions has recently been reported based on the observation of a spatial offset between the dark matter halo and the stars in a galaxy in the cluster Abell 3827. Interpreting the offset as due to dark matter self-interactions leads to a cross section measurement of ${\ensuremath{\sigma}}_{\mathrm{DM}}/m\ensuremath{\sim}(1--1.5)\text{ }\text{ }{\mathrm{cm}}^{2}\text{ }{\mathrm{g}}^{\ensuremath{-}1}$, where $m$ is the mass of the dark matter particle. We use this observation to constrain singlet scalar dark matter coupled to the standard model and to two-Higgs-doublet models. We show that the most natural scenario in this class of models is very light dark matter, below about 0.1 GeV, whose relic abundance is set by freeze-in, i.e., by slow production of dark matter in the early universe via extremely tiny interactions with the Higgs boson, never reaching thermal equilibrium. We also show that the dark matter abundance can be established through the usual thermal freeze-out mechanism in the singlet scalar extension of the Yukawa-aligned two-Higgs-doublet model, but that it requires rather severe fine tuning of the singlet scalar mass.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".