Unsupervised Spectral Unmixing for Telluric Correction using a Neural Network Autoencoder

The absorption of light by molecules in the atmosphere of Earth is a complication for ground-based observations of astrophysical objects. Comprehensive information on various molecular species is required to correct for this so called telluric absorption. We present a neural network autoencoder approach for extracting a telluric transmission spectrum from a large set of high-precision observed solar spectra from the HARPS-N radial velocity spectrograph. We accomplish this by reducing the data into a compressed representation, which allows us to unveil the underlying solar spectrum and simultaneously uncover the different modes of variation in the observed spectra relating to the absorption of H2O and O2 in the atmosphere of Earth. We demonstrate how the extracted components can be used to remove H2O and O2 tellurics in a validation observation with similar accuracy and at less computational expense than a synthetic approach with molecfit.