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Record W2483741191 · doi:10.1088/0004-637x/809/1/80

POPULATION SYNTHESIS OF CATACLYSMIC VARIABLES. I. INCLUSION OF DETAILED NUCLEAR EVOLUTION

2015· article· en· W2483741191 on OpenAlexaff
Jonas Goliasch, L. A. Nelson

Bibliographic record

VenueThe Astrophysical Journal · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGamma-ray bursts and supernovae
Canadian institutionsBishop's University
Fundersnot available
KeywordsPopulationAstrophysicsPhysicsDemography

Abstract

fetched live from OpenAlex

We have carried out an extensive population synthesis study of the ensemble properties of the present-day population of cataclysmic variables (PDCVs) that takes into account the nuclear evolution of high-mass donors close to the bifurcation and dynamical instability limits. Assuming the interrupted magnetic braking paradigm, we confirm many of the general features associated with the observed CV population and find enormous diversity in their secular properties. We predict that nearly half of the non-magnetic CVs with Porb > 6 hours are at least mildly evolved (i.e., greater than one-half of their MS turn-off age). Some of these systems contribute to the observed population of PDCVs in the period gap. We also see an enhancement by up to a factor of two in the probability of detecting CVs at the `minimum period'. This spike is quite narrow (approximately 5 minutes) and is attenuated because of the spectrum of WD masses and partly by the evolution of the donors. Our syntheses imply that there should be a very rapid decline in the number of ultracompact CVs (such as AM CVns). We find that between 0.05 to 1 percent of PDCVs could be UCs, and thus it is likely that the CV channel is probably not the primary contributor to the intrinsic population of UCs (especially for Porb < 30 minutes). Finally, a preliminary analysis of our results suggests that WDs in PDCVs experience a net gain in mass of less than about 0.1 Msun as a result of high mass-transfer rates early in their evolution.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.506
Threshold uncertainty score0.283

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 head0.219
Teacher spread0.209 · 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 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

Citations113
Published2015
Admission routes1
Has abstractyes

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