129 citations · 210 across the 9 of their papers we have counts for
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astro-ph.IM2019★ 25 cited
Assessing the performance of LTE and NLTE synthetic stellar spectra in a machine learning framework
Spencer Bialek, Sébastien Fabbro, Kim A. Venn +3
In the current era of stellar spectroscopic surveys, synthetic spectral libraries are the basis for the derivation of stellar parameters and chemical abundances. In this paper, we…
astro-ph.IM2017★ 129 cited
An Application of Deep Neural Networks in the Analysis of Stellar Spectra
Sebastien Fabbro, Kim Venn, Teaghan O'Briain +4
Spectroscopic surveys require fast and efficient analysis methods to maximize their scientific impact. Here we apply a deep neural network architecture to analyze both SDSS-III APO…