POPULATION SYNTHESIS OF CATACLYSMIC VARIABLES. I. INCLUSION OF DETAILED NUCLEAR EVOLUTION
Bibliographic record
Abstract
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 cataclysmic variable (CV) population and find enormous diversity in their secular properties. We predict that nearly half of the non-magnetic CVs with hr are at least mildly evolved (i.e., >50% 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 ( 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 and 1% 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 P orb < 30 minutes). Finally, a preliminary analysis of our results suggests that WDs in PDCVs experience a net gain in mass of as a result of high mass-transfer rates early in their evolution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".