Kavli Affiliate: Andrew Vanderburg
| First 5 Authors: Mariona Badenas-Agusti, Siyi Xu, Andrew Vanderburg, Kishalay De, Patrick Dufour
| Summary:
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 3,800r{A} to 9,000r{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 ($>2sigma$) 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.
| Search Query: ArXiv Query: search_query=au:”Andrew Vanderburg”&id_list=&start=0&max_results=3