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Record W2986913090 · doi:10.3847/2041-8213/ab571c

Evolving LMXBs: CARB Magnetic Braking

2019· article· en· W2986913090 on OpenAlexafffund
Kenny Van, Natalia Ivanova

Bibliographic record

VenueThe Astrophysical Journal Letters · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAstrophysicsPhysicsNeutron starLow MassMagnetic fieldMass transferDegenerate energy levelsRADIUSRotation (mathematics)ConvectionWhite dwarfStarsMechanicsGeometryComputer science

Abstract

fetched live from OpenAlex

Abstract The formation of low-mass X-ray binaries (LMXBs) is an ongoing challenge in stellar evolution. An important subset of LMXBs is the binary systems with a neutron star (NS) accretor. In NS LMXBs with nondegenerate donors, the mass transfer (MT) is mainly driven by magnetic braking (MB). The discrepancies between the observed MT rates and the theoretical models were known for a while. Theory predictions of the MT rates are too weak and differ by an order of magnitude or more. Recently, we showed that with the standard MB, it is not possible to find progenitor binary systems such that they could reproduce—at any time of their evolution—most of the observed persistent NS LMXBs. In this Letter we present a modified MB prescription, Convection And Rotation Boosted (CARB). CARB MB combines two recent improvements in understanding stellar magnetic fields and magnetized winds—the dependence of the magnetic field strength on the outer convective zone and the dependence of the Alfvèn radius on the donor’s rotation. Using this new MB prescription, we can reproduce the observed MT rates at the detected mass ratio and orbital period for all well-observed to-the-date Galactic persistent NS LMXBs. For the systems where the effective temperature of the donor stars is known, theory agrees with observations as well.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.933

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.007
GPT teacher head0.205
Teacher spread0.198 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations50
Published2019
Admission routes2
Has abstractyes

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