A Machine-Learning Compositional Study of Exoplanetary Material Accreted Onto Five Helium-Atmosphere White Dwarfs with $\text{cecilia}$

Abstract

We present the first application of the Machine Learning (ML) pipeline $\text{cecilia}$ 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 $\text{SDSS}$ (R=2,000) and $\text{Keck/ESI}$ (R=4,500) optical spectra, covering the wavelength range from about 3,800à to 9,000à . 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 $\text{Keck/ESI}$ 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$\ddot{I}ƒ$. We also find evidence of statistically significant ($>2\ddot{I}ƒ$) 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, $\text{cecilia}$ aspires to unlock population-wide studies of polluted WDs, therefore helping to improve our statistical knowledge of extrasolar compositions.

28 pages, 14 figures, 5 tables. Accepted for publication in MNRAS