Consistent accretion-induced heating of the neutron-star crust in MXB 1659−29 during two different outbursts
Bibliographic record
Abstract
Monitoring the cooling of neutron-star crusts heated during accretion outbursts allows us to infer the physics of the dense matter present in the crust. We examine the crust cooling evolution of the low-mass X-ray binary MXB 1659−29 up to ∼505 days after the end of its 2015 outburst (hereafter outburst II) and compare it with what we observed after its previous 1999 outburst (hereafter outburst I) using data obtained from theSwift,XMM-Newton, andChandraobservatories. The observed effective surface temperature of the neutron star in MXB 1659 − 29 dropped from ∼92 eV to ∼56 eV from ∼12 days to ∼505 days after the end of outburst II. The most recently performed observation after outburst II suggests that the crust is close to returning to thermal equilibrium with the core. We model the crust heating and cooling for both its outbursts collectively to understand the effect of parameters that may change for every outburst (e.g. the average accretion rate, the length of outburst, the envelope composition of the neutron star at the end of the outburst) and those which can be assumed to be the same during these two outbursts (e.g. the neutron star mass, its radius). Our modelling indicates that all parameters were consistent between the two outbursts with no need for any significant changes. In particular, the strength and the depth of the shallow heating mechanism at work (in the crust) were inferred to be consistent during both outbursts, contrary to what has been found when modelling the cooling curves after multiple outburst of another source, MAXI J0556−332. This difference in source behaviour is not understood. We discuss our results in the context of our current understanding of cooling of accretion-heated neutron-star crusts, and in particular with respect to the unexplained shallow heating mechanism.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".