A machine-learning compositional study of exoplanetary material accreted onto five helium-atmosphere white dwarfs with <tt>cecilia</tt>
Bibliographic record
Abstract
ABSTRACT We present the first application of the machine learning (ML) pipeline cecilia to determine the physical parameters and photospheric composition of five metal-polluted He-atmosphere white dwarfs without well-characterized elemental abundances. To achieve this, we perform a joint and iterative Bayesian fit to their SDSS (R = 2000) and Keck/ESI (R = 4500) optical spectra, covering the wavelength range from about 3800 to 9000 Å. Our analysis measures the abundances of at least two – and up to six – chemical elements in their atmospheres with a predictive accuracy similar to that of conventional WD analysis techniques ($\approx$0.20 dex). The white dwarfs with the largest number of detected heavy elements are SDSS J0859$+$5732 and SDSS J2311–0041, which simultaneously exhibit O, Mg, Si, Ca, and Fe in their Keck/ESI spectra. For all systems, we find that the bulk composition of their pollutants is largely consistent with those of primitive CI chondrites to within 1–2$\sigma$. We also find evidence of statistically significant ($>2\sigma$) oxygen excesses for SDSS J0859$+$5732 and SDSS J2311–0041, which could point to the accretion of oxygen-rich exoplanetary material. In the future, as wide-field astronomical surveys deliver millions of public WD spectra to the scientific community, cecilia aspires to unlock population-wide studies of polluted WDs, therefore helping to improve our statistical knowledge of extrasolar compositions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".