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Record W1970012634 · doi:10.1086/345345

Wind Accretion and State Transitions in Cygnus X‐1

2003· article· en· W1970012634 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAccretion (finance)Flux (metallurgy)SupergiantEmission spectrumLight curveIonizationAccretion discSpectral line

Abstract

fetched live from OpenAlex

We present the results of a spectroscopic monitoring program (from 1998 to 2002) of the H-alpha emission strength in HDE 226868, the optical counterpart of the black hole binary, Cyg X-1. The H-alpha profiles consist of (1) a P Cygni component associated with the wind of the supergiant, (2) emission components that attain high velocity at the conjunctions and that probably form in enhanced outflows both towards and away from the black hole, and (3) an emission component that moves in anti-phase with the supergiant's motion. We argue that the third component forms in accreted gas near the black hole, and the radial velocity curve of the emission is consistent with a mass ratio of M_X / M_opt = 0.36 +/- 0.05. We find that there is a general anti-correlation between the H-alpha emission strength and X-ray flux in the sense that when the H-alpha emission is strong (W_\lambda < -0.5 Angstroms) the X-ray flux is weaker and the spectrum harder. On the other hand, there is no correlation between H-alpha emission strength and X-ray flux when H-alpha is weak. During the low/hard X-ray state, the strong wind is fast and the accretion rate is relatively low, while in the high/soft state the weaker, highly ionized wind attains only a moderate velocity and the accretion rate increases. We argue that the X-ray transitions from the normal low/hard to the rare high/soft state are triggered by episodes of decreased mass loss rate in the supergiant donor star.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score0.357

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.223
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 teacher head, 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

Citations91
Published2003
Admission routes1
Has abstractyes

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