Detection and preliminary characterization of polluted white dwarfs from <i>Gaia</i> EDR3 and LAMOST
Bibliographic record
Abstract
ABSTRACT We present a catalogue of 62 polluted white dwarfs observed by the 9th Low-Resolution Data Release of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST LRS DR9v1; R≈1800) and the Early Data Release 3 (EDR3) of the Gaia Mission. Among these stellar remnants, 30 are new discoveries with previously unknown traces of calcium pollution. To generate our catalogue, we used a data base of 4324 unique Gaia EDR3 white dwarf candidates with LAMOST LRS DR9v1 observations, many of which have been spectroscopically confirmed by other telescopes. For these stars, we developed a quantitative method to detect calcium absorption in their spectra between 3900–4000 Å, which we then validated through visual inspection and multiple literature cross-checks. Our catalogue provides the astrometric and photometric properties of the white dwarf candidates, incorporates supplementary data (e.g. Montreal White Dwarf Database, MWDD; PanSTARRS; the Hubble Space Telescope), and indicates the possibility of calcium pollution in their atmospheres. For our final sample of polluted white dwarfs, we also determine the main atmospheric properties of 23 sources with effective temperatures Teff ≤ 25 000 K and no existing calcium abundances in the MWDD. Our analysis represents a first step towards measuring the full atmospheric composition of these stars and learning about the bulk properties of their accreted material. As we venture into the era of wide-field spectroscopic surveys, our work highlights the importance of combining large-scale data bases for identifying and characterizing new polluted 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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".