  Deep learning in the form of 1D convolutional neural networks have previously
been shown to be capable of efficiently classifying the evolutionary state of
oscillating red giants into red giant branch stars and helium-core burning
stars by recognizing visual features in their asteroseismic frequency spectra.
We elaborate further on the deep learning method by developing an improved
convolutional neural network classifier. To make our method useful for current
and future space missions such as K2, TESS and PLATO, we train classifiers that
are able to classify the evolutionary states of lower frequency resolution
spectra expected from these missions. Additionally, we provide new
classifications for 8633 Kepler red giants, out of which 426 have previously
not been classified using asteroseismology. This brings the total to 14983
Kepler red giants classified with our new neural network. We also verify that
our classifiers are remarkably robust to suboptimal data, including low
signal-to-noise and incorrect training truth labels.
