In an era when spectroscopic surveys are capable of collecting spectra for hundreds
of thousands of stars, fast and efficient analysis methods are required to maximize
scientific impact. These surveys provide a homogeneous database of stellar spectra
that are ideal for machine learning applications. One such application, StarNet, is
a convolutional neural network developed to derive stellar labels (temperature, surface
gravity, and metallicity) from both SDSS-III APOGEE and synthetic stellar spectra.
It has demonstrated excellent precision and accuracy over a wide range of signal-to-noise
ratios, when trained on either observed or synthetic spectra. Though StarNet was developed
using the high-resolution (R similar to 20, 000) APOGEE spectra and corresponding
ASSeT synthetic grid, we suggest that this technique is applicable to other spectral
resolutions, spectral surveys, and wavelength regimes. As a demonstration, we present
a version of StarNet trained on lower resolution, R=6000, ASSeT synthetic spectra.
This resolution was selected to prepare for spectra delivered by Gemini/NIFS and the
forthcoming Gemini/GIRMOS instruments. Results suggest that the stellar parameters
determined from this medium-resolution StarNet version can be comparable in precision
to the high-resolution APOGEE results. This success can be attributed to (1) a large
training set of synthetic spectra (N similar to 200,000) with a priori stellar labels,
and (2) the use of the entire spectrum in the solution rather than a few weighted
windows, which is common in other automated spectral analysis methods (e.g. FERRE).
Remaining challenges in our StarNet applications include rectification, continuum
normalization, and wavelength coverage. Here with present preliminary results on the
impact of imperfect continuum rectification when compared to normalized synthetic
data. Solutions to these problems will contribute to efficient spectroscopic surveys,
their data reduction pipelines, and the precision in their post-data products (for
the planned Maunakea Spectroscopic Explorer).
