  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 APOGEE DR13 and synthetic stellar spectra. When our
convolutional neural network model (StarNet) is trained on APOGEE spectra, we
show that the stellar parameters (temperature, gravity, and metallicity) are
determined with similar precision and accuracy as the APOGEE pipeline. StarNet
can also predict stellar parameters when trained on synthetic data, with
excellent precision and accuracy for both APOGEE data and synthetic data, over
a wide range of signal-to-noise ratios. In addition, the statistical
uncertainties in the stellar parameter determinations are comparable to the
differences between the APOGEE pipeline results and those determined
independently from optical spectra. We compare StarNet to other data-driven
methods; for example, StarNet and the Cannon 2 show similar behaviour when
trained with the same datasets, however StarNet performs poorly on small
training sets like those used by the original Cannon. The influence of the
spectral features on the stellar parameters is examined via partial derivatives
of the StarNet model results with respect to the input spectra. While StarNet
was developed using the APOGEE observed spectra and corresponding ASSET
synthetic data, we suggest that this technique is applicable to other
wavelength ranges and other spectral surveys.
