APOGEE Net: Improving the derived spectral parameters for young stars
  through deep learning
  Machine learning allows efficient extraction of physical properties from
stellar spectra that have been obtained by large surveys. The viability of ML
approaches has been demonstrated for spectra covering a variety of wavelengths
and spectral resolutions, but most often for main sequence or evolved stars,
where reliable synthetic spectra provide labels and data for training. Spectral
models of young stellar objects (YSOs) and low mass main sequence (MS) stars
are less well-matched to their empirical counterparts, however, posing barriers
to previous approaches to classify spectra of such stars. In this work we
generate labels for YSOs and low mass MS stars through their photometry. We
then use these labels to train a deep convolutional neural network to predict
log g, Teff, and Fe/H for stars with APOGEE spectra in the DR14 dataset. This
"APOGEE Net" has produced reliable predictions of log g for YSOs, with
uncertainties of within 0.1 dex and a good agreement with the structure
indicated by pre-main sequence evolutionary tracks, and correlate well with
independently derived stellar radii. These values will be useful for studying
pre-main sequence stellar populations to accurately diagnose membership and
ages.
