Neutron Star Internal Heating Constraints on Mirror Matter
Bibliographic record
Abstract
Mirror sectors have been proposed to address the problems of dark matter, baryogenesis, and the neutron lifetime anomaly. In this work we study a new, powerful probe of mirror neutrons: neutron star temperatures. When neutrons in the neutron star core convert to mirror neutrons during collisions, the vacancies left behind in the nucleon Fermi seas are refilled by more energetic nucleons, releasing immense amounts of heat in the process. We derive a new constraint on the allowed strength of neutron-mirror-neutron mixing from observations of the coldest (sub-40 000 Kelvin) neutron star, PSR 2144-3933. Our limits compete with laboratory searches for neutron-mirror-neutron transitions but apply to a range of mass splittings between the neutron and mirror neutron that is 19 orders of magnitude larger. This heating mechanism, also pertinent to other neutron disappearance channels such as exotic neutron decay, provides a compelling physics target for upcoming ultraviolet, optical, and infrared telescopes to study thermal emissions of cold neutron stars.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".