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Record W2036857553 · doi:10.1086/517491

Millihertz Quasi‐periodic Oscillations from Marginally Stable Nuclear Burning on an Accreting Neutron Star

2007· article· en· W2036857553 on OpenAlexaff
Alexander Heger, A. Cumming, S. E. Woosley

Bibliographic record

VenueThe Astrophysical Journal · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsMcGill University
Fundersnot available
KeywordsNeutron starPhysicsAstrophysicsAccretion (finance)Oscillation (cell signaling)LuminosityStarsGalaxy

Abstract

fetched live from OpenAlex

We investigate marginally stable nuclear burning on the surface of accreting neutron stars as an explanation for the mHz quasi-periodic oscillations (QPOs) observed from three low mass X-ray binaries. At the boundary between unstable and stable burning, the temperature dependence of the nuclear heating rate and cooling rate almost cancel. The result is an oscillatory mode of burning, with an oscillation period close to the geometric mean of the thermal and accretion timescales for the burning layer. We describe a simple one-zone model which illustrates this basic physics, and then present detailed multizone hydrodynamical calculations of nuclear burning close to the stability boundary using the KEPLER code. Our models naturally explain the characteristic 2 minute period of the mHz QPOs, and why they are seen only in a very narrow range of X-ray luminosities. The oscillation period is sensitive to the accreted hydrogen fraction and the surface gravity, suggesting a new way to probe these parameters. A major puzzle is that the accretion rate at which the oscillations appear in the theoretical models is an order of magnitude larger than the rate implied by the X-ray luminosity when the mHz QPOs are seen. We discuss the implications for our general understanding of nuclear burning on accreting neutron stars. One possibility is that the accreted material covers only part of the neutron star surface at luminosities Lx > ~1E37 erg/s.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.860
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.241
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations93
Published2007
Admission routes1
Has abstractyes

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