cecilia: A Machine Learning-Based Pipeline for Measuring Metal
  Abundances of Helium-rich Polluted White Dwarfs
M. Badenas-Agusti J. Viaña A. Vanderburg S. Blouin P. Dufour S. Xu L. Sha
Over the past several decades, conventional spectral analysis techniques of
polluted white dwarfs have become powerful tools to learn about the geology and
chemistry of extrasolar bodies. Despite their proven capabilities and extensive
legacy of scientific discoveries, these techniques are however still limited by
their manual, time-intensive, and iterative nature. As a result, they are
susceptible to human errors and are difficult to scale up to population-wide
studies of metal pollution. This paper seeks to address this problem by
presenting cecilia, the first Machine Learning (ML)-powered spectral modeling
code designed to measure the metal abundances of intermediate-temperature
(10,000$\leq T_{\rm eff} \leq$20,000 K), Helium-rich polluted white dwarfs.
Trained with more than 22,000 randomly drawn atmosphere models and stellar
parameters, our pipeline aims to overcome the limitations of classical methods
by replacing the generation of synthetic spectra from computationally expensive
codes and uniformly spaced model grids, with a fast, automated, and efficient
neural-network-based interpolator. More specifically, cecilia combines
state-of-the-art atmosphere models, powerful artificial intelligence tools, and
robust statistical techniques to rapidly generate synthetic spectra of polluted
white dwarfs in high-dimensional space, and enable accurate ($\lesssim$0.1 dex)
and simultaneous measurements of 14 stellar parameters -- including 11
elemental abundances -- from real spectroscopic observations. As massively
multiplexed astronomical surveys begin scientific operations, cecilia's
performance has the potential to unlock large-scale studies of extrasolar
geochemistry and propel the field of white dwarf science into the era of Big
Data. In doing so, we aspire to uncover new statistical insights that were
previously impractical with traditional white dwarf characterisation
techniques.