A Machine-Learning Compositional Study of Exoplanetary Material Accreted Onto Five Helium-Atmosphere White Dwarfs with $\texttt{cecilia}$
This work addresses the challenge of characterizing exoplanetary compositions from white dwarf spectra, which is incremental as it applies an existing ML method to new astronomical data.
The researchers applied the machine learning pipeline cecilia to analyze the composition of exoplanetary material accreted onto five helium-atmosphere white dwarfs, achieving predictive accuracy similar to conventional methods (≈0.20 dex) and finding pollutant compositions consistent with primitive CI chondrites, with evidence of oxygen excesses in two systems.
We present the first application of the Machine Learning (ML) pipeline $\texttt{cecilia}$ 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 $\textit{SDSS}$ (R=2,000) and $\textit{Keck/ESI}$ (R=4,500) optical spectra, covering the wavelength range from about 3,800Å to 9,000Å. 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 $\textit{Keck/ESI}$ 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$σ$. 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, $\texttt{cecilia}$ aspires to unlock population-wide studies of polluted WDs, therefore helping to improve our statistical knowledge of extrasolar compositions.