# A Machine-Learning Compositional Study of Exoplanetary Material Accreted Onto Five Helium-Atmosphere White Dwarfs with textttcecilia

arXiv: https://arxiv.org/abs/2505.06228 · HTML: https://mlsanity.com/paper/2505.06228/

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- First posted on arXiv 9 May 2025.

## Abstract (arXiv)

We present the first application of the Machine Learning (ML) pipeline textttcecilia to determine the physical parameters and photospheric composition of five metal-polluted He-atmosphere white dwarfs without well-characterised elemental abundances. To achieve this, we perform a joint and iterative Bayesian fit to their textitSDSS (R=2,000) and textitKeck/ESI (R=4,500) optical spectra, covering the wavelength range from about 3,800rA to 9,000rA. 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 (≈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 textitKeck/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 1-2σ. We also find evidence of statistically significant (>2σ) 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, textttcecilia aspires to unlock population-wide studies of polluted WDs, therefore helping to improve our statistical knowledge of extrasolar compositions.