A Photometric and Spectroscopic Investigation of the DB White Dwarf Population Using SDSS and Gaia Data
Bibliographic record
Abstract
Abstract We present a comprehensive analysis of DB white dwarfs drawn from the Sloan Digital Sky Survey, based on model fits to ugriz photometry and medium-resolution spectroscopy from the Sloan Digital Sky Survey. We also take advantage of the exquisite trigonometric parallax measurements recently obtained by the Gaia mission. Using the so-called photometric and spectroscopic techniques, we measure the atmospheric and physical parameters of each object in our sample (T eff, , , Ca/He, R, M), and compare the values obtained from both techniques in order to assess the precision and accuracy of each method. We then explore in great detail the surface gravity, stellar mass, and hydrogen abundance distributions of DB white dwarfs as a function of effective temperature. We present some clear evidence for a large population of unresolved double-degenerate binaries composed of DB+DB and even DB+DA white dwarfs. In the light of our results, we finally discuss the spectral evolution of DB white dwarfs, in particular the evolution of the DB-to-DA ratio as a function of T eff, and we revisit the question of the origin of hydrogen in DBA white dwarfs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".