Classification and parameterization of a large <i>Gaia</i> sample of white dwarfs using XP spectra
Bibliographic record
Abstract
Context. The latest Gaia data release in July 2022, DR3, in addition to the refinement of the astrometric and photometric parameters from DR2, added a number of important data products to those available in earlier releases, including radial velocity data, information on stellar multiplicity, and XP spectra of a selected sample of stars. Gaia has proved to be an important search tool for white dwarf stars, which are readily identifiable from their absolute G magnitudes as low luminosity objects in the Hertzsprung–Russell (H–R) diagram. Each data release has yielded large catalogs of white dwarfs, containing several hundred thousand objects, far in excess of the numbers known from all previous surveys (∼40 000). While the normal Gaia photometry ( G , G BP , and G RP bands) and astrometry can be used to identify white dwarfs with high confidence, it is much more difficult to parameterize the stars and determine the white dwarf spectral type from this information alone. Observing all stars in these catalogs with follow-up spectroscopy and photometry is also a huge logistical challenge with current facilities. Aims. The availability of the XP spectra and synthetic photometry presents an opportunity for a more detailed spectral classification and measurement of the effective temperature and surface gravity of Gaia white dwarfs. Methods. A magnitude limit of G < 17.6 was applied to the routine production of XP spectra for Gaia sources, which would have excluded most white dwarfs. Therefore, we created a catalog of 100 000 high-quality white dwarf identifications for which XP spectra were processed, with a magnitude limit of G < 20.5. Synthetic photometry was computed for all these stars, from the XP spectra, in Johnson, SDSS, and J-PAS, published as the Gaia Synthetic Photometry Catalog – White Dwarfs (GSPC-WD). We took this catalog and applied machine learning techniques to provide a classification of all the stars from the XP spectra. We have then applied an automated spectral fitting program, with χ -squared minimization, to measure their physical parameters (effective temperature and log g ) from which we could estimate the white dwarf masses and radii. Results. We present the results of this work, demonstrating the power of being able to classify and parameterize such a large sample of ≈100 000 stars. We describe what we can learn about the white dwarf population from this dataset. We also explored the uncertainties in the process and the limitations of the dataset.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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".