Thermonuclear Stability of Material Accreting onto a Neutron Star
Bibliographic record
Abstract
We present a global linear stability analysis of nuclear fuel accumulating on the surface of an accreting neutron star, and we identify the conditions under which thermonuclear bursts are triggered. The analysis reproduces all the recognized regimes of hydrogen and helium bursts and in addition shows that at high accretion rates, near the limit of stable burning, there is a regime of "delayed mixed bursts" that is distinct from the more usual "prompt mixed bursts." In delayed mixed bursts, a large fraction of the fuel is burned stably before the burst is triggered. Bursts thus have longer recurrence times but at the same time have somewhat smaller fluences. Therefore, the parameter α, which measures the ratio of the energy released via accretion to that generated through nuclear reactions in the burst, is up to an order of magnitude larger than for prompt bursts. This increase in α near the threshold of stable burning has been seen in observations. We explore a wide range of mass accretion rates, neutron star radii, and core temperatures and calculate a variety of burst properties. From a preliminary comparison with data, we suggest that bursting neutron stars may have hot cores, with T core ≳ 10 7.5 K, consistent with interior cooling via the modified Urca or similar low-efficiency process, rather than T core ~ 10 7 K, as expected for the direct Urca process. There is also an indication that neutron star radii are somewhat small, ≲10 km. Both of these conclusions need to be confirmed by comparing more careful calculations with better data.
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 | 0.000 | 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) | 0.001 | 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".