  A machine learning approach has been implemented to measure the electron
temperature directly from the emission spectra of a tokamak plasma. This
approach utilized a neural network (NN) trained on a dataset of 1865 time
slices from operation of the DIII-D tokamak using extreme ultraviolet / vacuum
ultraviolet (EUV/VUV) emission spectroscopy matched with high-accuracy divertor
Thomson scattering measurements of the electron temperature, $T_e$. This NN is
shown to be particularly good at predicting $T_e$ at low temperatures ($T_e <
10$ eV) where the NN demonstrated a mean average error of less than 1 eV.
Trained to detect plasma detachment in the tokamak divertor, a NN classifier
was able to correctly identify detached states ($T_e<5$ eV) with a 99% accuracy
(F$_1$ score of 0.96) at an acquisition rate $10\times$ faster than the Thomson
scattering measurement. The performance of the model is understood by examining
a set of 4800 theoretical spectra generated using collisional radiative
modeling that was also used to predict the performance of a low-cost
spectrometer viewing nitrogen emission in the visible wavelengths. These
results provide a proof-of-principle that low-cost spectrometers leveraged with
machine learning can be used both to boost the performance of more expensive
diagnostics on fusion devices, and be used independently as a fast and accurate
$T_e$ measurement and detachment classifier.
