The Next Generation Virgo Cluster Survey. XXVIII. Characterization of the Galactic White Dwarf Population
Bibliographic record
Abstract
Abstract We use three different techniques to identify hundreds of white dwarf (WD) candidates in the Next Generation Virgo Cluster Survey (NGVS) based on photometry from the NGVS and GUViCS, and proper motions derived from the NGVS and the Sloan Digital Sky Survey (SDSS). Photometric distances for these candidates are calculated using theoretical color–absolute magnitude relations, while effective temperatures are measured by fitting their spectral energy distributions. Disk and halo WD candidates are separated using a tangential velocity cut of 200 km s−1 in a reduced proper motion diagram, which leads to a sample of six halo WD candidates. Cooling ages, calculated for an assumed WD mass of 0.6M ⊙, range between 60 Myr and 6 Gyr, although these estimates depend sensitively on the adopted mass. Luminosity functions for the disk and halo subsamples are constructed and compared to previous results from the SDSS and SuperCOSMOS survey. We compute a number density of (2.81 ± 0.52) × 10−3 pc−3 for the disk WD population—consistent with previous measurements. We find (7.85 ± 4.55) × 10−6 pc−3 for the halo, or 0.3% of the disk. Observed stellar counts are also compared to predictions made by the TRILEGAL and Besançon stellar population synthesis models. The comparison suggests that the TRILEGAL model overpredicts the total number of WDs. The WD counts predicted by the Besançon model agree with the observations, although a discrepancy arises when comparing the predicted and observed halo WD populations; the difference is likely due to the WD masses in the adopted model halo.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".