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Record W3215962913 · doi:10.3847/1538-4357/ac236c

Constraining Progenitors of Observed Low-mass X-ray Binaries Using Convection and Rotation-Boosted Magnetic Braking

2021· article· en· W3215962913 on OpenAlexafffund
Kenny Van, Natalia Ivanova

Bibliographic record

VenueThe Astrophysical Journal · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAstrophysicsPhysicsLow MassPopulationMass transferRotation (mathematics)Binary numberStellar massAstronomyStarsStar formationMechanics

Abstract

fetched live from OpenAlex

Abstract We present a new method for constraining the mass transfer evolution of low-mass X-ray binaries (LMXBs)—a reverse population synthesis technique. This is done using the detailed 1D stellar evolution code MESA (Modules for Experiments in Stellar Astrophysics) to evolve a high-resolution grid of binary systems spanning a comprehensive range of initial donor masses and orbital periods. We use the recently developed convection and rotation-boosted (CARB) magnetic braking scheme. The CARB magnetic braking scheme is the only magnetic braking prescription capable of reproducing an entire sample of well-studied persistent LMXBs—those with mass ratios, periods, and mass transfer rates that have been observationally determined. Using the reverse population synthesis technique, where we follow any simulated system that successfully reproduces an observed LMXB backward, we have constrained possible progenitors for each observed well-studied persistent LMXB. We also determined that the minimum number of LMXB formations in the Milky Way is 1500 per Gyr if we exclude Cyg X-2. For Cyg X-2, the most likely formation rate is 9000 LMXB Gyr−1. The technique we describe can be applied to any observed LMXB with well-constrained mass ratio, period, and mass transfer rate. With the upcoming GAIA DR3 containing information on binary systems, this technique can be applied to the data release to search for progenitors of observed persistent LMXBs.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.026
GPT teacher head0.239
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations11
Published2021
Admission routes2
Has abstractyes

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