A Volume-limited Sample of Cataclysmic Variables from Gaia DR2: Space Density and Population Properties
Bibliographic record
Abstract
ABSTRACT We present the first volume-limited sample of cataclysmic variables (CVs), selected using the accurate parallaxes provided by the second data release (DR2) of the European Space Agency Gaia space mission. The sample is composed of 42 CVs within 150 pc, including two new systems discovered using the Gaia data, and is $(77 \pm 10)$ per cent complete. We use this sample to study the intrinsic properties of the Galactic CV population. In particular, the CV space density we derive, $\rho =(4.8^{+0.6}_{-0.8}) \times 10^{-6}\, \mbox{$\mathrm{pc}^{-3}$}$, is lower than that predicted by most binary population synthesis studies. We also find a low fraction of period bounce CVs, seven per cent, and an average white dwarf mass of $\langle M_\mathrm{WD} \rangle = (0.83 \pm 0.17)\, \mathrm{M}_\odot$. Both findings confirm previous results, ruling out the presence of observational biases affecting these measurements, as has been suggested in the past. The observed fraction of period bounce CVs falls well below theoretical predictions, by at least a factor of five, and remains one of the open problems in the current understanding of CV evolution. Conversely, the average white dwarf mass supports the presence of additional mechanisms of angular momentum loss that have been accounted for in the latest evolutionary models. The fraction of magnetic CVs in the 150 pc sample is remarkably high at 36 per cent. This is in striking contrast with the absence of magnetic white dwarfs in the detached population of CV progenitors, and underlines that the evolution of magnetic systems has to be included in the next generation of population models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".