Deep learning of multi-element abundances from high-resolution
  spectroscopic data
  Deep learning with artificial neural networks is increasingly gaining
attention, because of its potential for data-driven astronomy. However, this
methodology usually does not provide uncertainties and does not deal with
incompleteness and noise in the training data. In this work, we design a neural
network for high-resolution spectroscopic analysis using APOGEE data that
mimics the methodology of standard spectroscopic analyses: stellar parameters
are determined using the full wavelength range, but individual element
abundances use censored portions of the spectrum. We train this network with a
customized objective function that deals with incomplete and noisy training
data and apply dropout variational inference to derive uncertainties on our
predictions. We determine parameters and abundances for 18 individual elements
at the $\approx 0.03$ dex level, even at low signal-to-noise ratio. We
demonstrate that the uncertainties returned by our method are a realistic
estimate of the precision and they automatically blow up when inputs or outputs
outside of the training set are encountered, thus shielding users from unwanted
extrapolation. By using standard deep-learning tools for GPU acceleration, our
method is extremely fast, allowing analysis of the entire APOGEE data set of
$\approx250,000$ spectra in ten minutes on a single, low-cost GPU. We release
the stellar parameters and 18 individual-element abundances with associated
uncertainty for the entire APOGEE DR14 dataset. Simultaneously, we release
astroNN, a well-tested, open-source python package developed for this work, but
that is also designed to be a general package for deep learning in astronomy.
astroNN is available at https://github.com/henrysky/astroNN with extensive
documentation at http://astroNN.readthedocs.io.
