Speaker
Description
Artificial Neural Networks have been applied in many fields of science and are particularly successful in image processing. Here we outline the challenges of Deep Learning in stellar spectroscopy, since stellar spectra are fundamentally different from images. Although only one-dimensional, spectra show no translation invariance and important features appear on all scales: While the surface temperature of a star can be told either from the overall shape of the spectrum or the strengths of certain easily detectable absorption lines, other physical parameters, such as chemical element abundances, are encoded in many small features scattered at a multitude of wavelengths. We also consider applications other than physical parameter inference: denoising and normalization with autoencoders and surrogate modelling with generative networks.
