Dark matter or regular matter in neutron stars? How to tell the difference from the coalescence of compact objects
Bibliographic record
Abstract
The mirror twin Higgs model is a candidate for (strongly-interacting) complex dark matter, which mirrors SM interactions with heavier quark masses. A consequence of this model are mirror neutron stars---exotic stars made entirely of mirror matter, which are significantly smaller than neutron stars and electromagnetically dark. This makes mergers of two mirror neutron stars detectable and distinguishable in gravitational wave observations, but can we observationally distinguish between regular neutron stars and those that may contain some mirror matter? This is the question we study in this paper, focusing on two possible realizations of mirror matter coupled to standard model matter within a compact object: (i) mirror matter captured by a neutron star and (ii) mirror neutron star-neutron star coalescences. Regarding (i), we find that (nonrotating) mirror-matter-admixed neutron stars no longer have a single mass-radius sequence, but rather exist in a two-dimensional mass-radius plane. Regarding (ii), we find that binary systems with mirror neutron stars would span a much wider range of chirp masses and completely different binary Love relations, allowing merger remnants to be very light black holes. The implications of this are that gravitational wave observations with advanced LIGO and Virgo, and x-ray observations with NICER, could detect or constrain the existence of mirror matter through searches with wider model and parameter priors.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".