A machine-learning compositional study of exoplanetary material accreted onto five helium-atmosphere white dwarfs with <tt>cecilia</tt>

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$. We also find evidence of statistically significant ($&amp;gt;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.