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Record W3100073352

New Low Accretion-Rate Magnetic Binary Systems and their Significance for the Evolution of Cataclysmic Variables

2005· article· en· W3100073352 on OpenAlexaff

Bibliographic record

VenueCERN Document Server (European Organization for Nuclear Research) · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysical Phenomena and Observations
Canadian institutionsYork University
Fundersnot available
KeywordsPhysicsWhite dwarfAstrophysicsIntermediate polarCataclysmic variable starAstronomyAccretion (finance)Common envelopeRoche lobeStars
DOInot available

Abstract

fetched live from OpenAlex

Discoveries of two new white dwarf plus M star binaries with striking optical cyclotron emission features from the Sloan Digital Sky Survey (SDSS) brings to six the total number of X-ray faint, magnetic accretion binaries that accrete at rates < 10^{-13} Msun/yr, or <1% of the values normally encountered in cataclysmic variables. This fact, coupled with donor stars that underfill their Roche lobes and very cool white dwarfs, brand the binaries as post common-envelope systems whose orbits have not yet decayed to the point of Roche-lobe contact. They are pre-magnetic CVs, or pre-Polars. The systems exhibit spin/orbit synchronism and apparently accrete by efficient capture of the stellar wind from the secondary star, a process that has been dubbed a ``magnetic siphon''. Because of this, period evolution of the binaries will occur solely by gravitational radiation, which is very slow for periods >3 hr. Optical surveys for the cyclotron harmonics appear to be the only means of discovery, so the space density of pre-Polars could rival that of Polars, and the binaries provide an important channel of progenitors (in addition to the asynchronous Intermediate Polars). Both physical and SDSS observational selection effects are identified that may help to explain the clumping of all six systems in a narrow range of magnetic field strength around 60 MG.

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 score0.695
Threshold uncertainty score0.539

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.015
GPT teacher head0.231
Teacher spread0.216 · 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

Citations64
Published2005
Admission routes1
Has abstractyes

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