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tokens: see that the optical analyses have larger scatter by a factor of ≳2 in T eff , ≳4 in log g , and ≳4 in [Fe/H]. The larger scatter suggests that our simplified derivation of statistical uncertainties is sufficient until we can calibrate stellar parameters for a larger set of more homogeneous benchmark stars. Well-calibrated stellar parameters would require a more thorough probabilistic model of both StarNet training and StarNet predictions. Therefore, we consider this very good agreement and do not investigate these intrinsic dispersions further at this time. A comparison of Figs 10 and 13 reveals that, when StarNet is trained on synthetic spectra and compared to DR12, the scatter is a factor of ∼2 lower than the residuals between the DR13 and optical analyses. However, due to the different input physics, analysis techniques, spectral resolution, etc. between StarNet, APOGEE, and optical analyses, we cannot directly compare the precision of the stellar parameters returned by StarNet and an optical analysis at this time. Rather, this similar degree of scatter in the residuals indicates that the systematic errors intrinsic to StarNet are comparable to the systematic errors expected from an optical analysis. This indicates that the StarNet model is sufficient for a single survey analysis and can provide a new and exciting tool for the efficient analyses of spectroscopic surveys. 5.2 M dwarfs in DR13 While APOGEE has primarily observed red giant and sub-giant stars, DR13 includes spectra for known M dwarfs. These stars are accompanied with a specific Target Flag: ‘APOGEE MDWARF’. Initially, we had not removed these stars from the data set and found obvious discrepancies between the StarNet results and those provided by ASPCAP DR13. On closer inspection, we found 5900 stars that resembled the known M dwarfs, but that had not been flagged as M dwarfs. Those stars were removed from our data sets, using one of the following cuts: Metallicity parameters of −0.9 (ASPCAP [Fe/H]) 1.0, and where (StarNet [Fe/H] − ASPCAP [Fe/H]) −0.4, or Gravity parameters of (ASPCAP log g ) >3.6, and where (StarNet log g − ASPCAP log g ) −0.3 The ASSET grid provided with the APOGEE DR12 data release does not include synthetic spectra for M dwarfs, and therefore, StarNet cannot be trained on those stars. Furthermore, StarNet cannot be trained on the observed spectra at this time since there are too few M dwarfs in the APOGEE data set (1000 flagged). Our method for flagging M dwarfs is not robust, e.g. some RGB stars may have been removed, while other M dwarfs were not. 5.3 NN considerations One limitation of a deep NN is the necessity of large training sets spanning a wide range of the parameter space. The distribution of the stellar parameters in the training set for StarNet (when trained on observed spectra) are shown in Fig. 14 . From these plots, it is evident that there are fewer spectra for stars at high temperatures, low metallicities, and low gravities. As seen in