Fast Neutrino Cooling in the Accreting Neutron Star MXB 1659-29
Bibliographic record
Abstract
Abstract Modeling of crust heating and cooling across multiple accretion outbursts of the low mass X-ray binary MXB 1659-29 indicates that the neutrino luminosity of the neutron star core is consistent with direct Urca (dUrca) reactions occurring in ∼1% of the core volume. We investigate this scenario with neutron star models that include a detailed equation of state parametrized by the slope of the nuclear symmetry energy L, and a range of neutron and proton superfluid gaps. We find that the predicted neutron star mass depends sensitively on L and the assumed gaps. We discuss which combinations of superfluid gaps reproduce the inferred neutrino luminosity. Larger values of L ≳ 80 MeV require superfluidity to suppress dUrca reactions in low mass neutron stars, i.e., the proton or neutron gap is sufficiently strong and extends to high enough density. However, the largest gaps give masses near the maximum mass, making it difficult to accommodate colder neutron stars. The heat capacities of our models span the range from fully paired to fully unpaired nucleons meaning that long-term observations of core cooling could distinguish between models. As a route to solutions with a larger emitting volume, which could provide a more natural explanation for the inferred neutrino luminosity, we discuss the possibility of alternative, less efficient, fast cooling processes in exotic cores. To be consistent with the inferred neutrino luminosity, such processes must be within a factor of ∼1000 of dUrca. We discuss the impact of future constraints on neutron star mass, radius, and the density dependence of the symmetry energy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".