Unsupervised Spectral Unmixing For Telluric Correction Using A Neural Network Autoencoder
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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 H_2O and O_2 in the atmosphere of Earth. We demonstrate how the extracted components can be used to remove H_2O and O_2 tellurics in a validation observation with similar accuracy and at less computational expense than a synthetic approach with molecfit.
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