Direct detection of atomic dark matter in white dwarfs
Bibliographic record
Abstract
Abstract Dark matter could have a dissipative asymmetric subcomponent in the form of atomic dark matter (aDM). This arises in many scenarios of dark complexity, and is a prediction of neutral naturalness, such as the Mirror Twin Higgs model. We show for the first time how White Dwarf cooling provides strong bounds on aDM. In the presence of a small kinetic mixing between the dark and SM photon, stars are expected to accumulate atomic dark matter in their cores, which then radiates away energy in the form of dark photons. In the case of white dwarfs, this energy loss can have a detectable impact on their cooling rate. We use measurements of the white dwarf luminosity function to tightly constrain the kinetic mixing parameter between the dark and visible photons, for DM masses in the range 10−5–105 GeV, down to values of ϵ ∼ 10−12. Using this method we can constrain scenarios in which aDM constitutes fractions as small as 10−3 of the total dark matter density. Our methods are highly complementary to other methods of probing aDM, especially in scenarios where the aDM is arranged in a dark disk, which can make direct detection extremely difficult but actually slightly enhances our cooling constraints.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".