Electromagnetic Signatures of Mirror Stars
Bibliographic record
Abstract
Abstract Mirror stars are a generic prediction of dissipative dark matter (DM) models, including minimal atomic DM and twin baryons in the mirror twin Higgs model. Mirror stars can capture regular matter from the interstellar medium through extremely suppressed kinetic mixing interactions between the regular and the dark photon. This accumulated “nugget” will draw heat from the mirror star core and emit highly characteristic X-ray and optical signals. In this work, we devise a general parameterization of mirror star nugget properties that is independent of the unknown details of mirror star stellar physics, and use the Cloudy spectral synthesis code to obtain realistic and comprehensive predictions for the thermal emissions from optically thin mirror star nuggets. We find that mirror star nuggets populate an extremely well-defined and narrow region of the Hertzsprung–Russell diagram that only partially overlaps with the white dwarf population. Our detailed spectral predictions, which we make publicly available, allow us to demonstrate that optically thin nuggets can be clearly distinguished from white dwarf stars by their continuum spectrum shape, and from planetary nebulae and other optically thin standard sources by their highly exotic emission-line ratios. Our work will enable realistic mirror star telescope searches, which may reveal the detailed nature of DM.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".