Neutron star observations of pseudoscalar-mediated dark matter
Bibliographic record
Abstract
Scattering interactions between dark matter and Standard Model states mediated by pseudoscalars are generically challenging to uncover at direct detection experiments due to rates suppressed by powers of the local dark matter velocity ${v}_{\mathrm{DM}}\ensuremath{\sim}{10}^{\ensuremath{-}3}c$. However, they may be observed in the dark matter-induced heating of neutron stars, whose steep gravitational potentials prevent such suppression by accelerating infalling particles to semirelativistic speeds. We investigate this phenomenon in the context of two specific, self-consistent scenarios for pseudoscalars coupled to dark matter, and compare the sensitivity of neutron star heating to bounds from direct searches for the mediators and dark matter. The first ``lighter'' scenario consists of sub-10 GeV mass dark matter mediated by an axionlike particle, while the second ``heavier'' scenario has dark matter above 10 GeV mediated by a dark pseudoscalar that mixes with a pseudoscalar from a two-Higgs doublet (the so-called $2\mathrm{HDM}+a$ model). In both frameworks, we show that imminent measurements of neutron stars will be able to test pseudoscalar-mediated dark matter beyond the reach of direct dark matter searches as well as bounds on the mediators from flavor observables, beam dump experiments, and high-energy colliders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".