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

TL;DR

This paper introduces the first use of the ML pipeline cecilia to analyze five helium-atmosphere white dwarfs, accurately determining their atmospheric compositions and revealing potential exoplanetary material signatures.

Contribution

The study applies a novel ML approach to derive atmospheric parameters and compositions of white dwarfs, achieving accuracy comparable to traditional methods and identifying possible oxygen-rich exoplanetary debris.

Findings

Detected multiple heavy elements in white dwarf atmospheres.

Found compositions consistent with primitive CI chondrites.

Identified significant oxygen excesses suggesting exoplanetary material.

Abstract

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 3,800\r{A} to 9,000\r{A}. 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$\sigma$. 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, $\texttt{cecilia}$ aspires to unlock population-wide studies of polluted WDs, therefore helping to improve our statistical knowledge of extrasolar compositions.