Unsupervised Spectral Unmixing For Telluric Correction Using A Neural Network Autoencoder
This addresses a complication in astrophysics for astronomers by providing a more efficient correction method, though it is incremental as it builds on existing autoencoder techniques.
The paper tackled the problem of correcting telluric absorption in ground-based astrophysical observations by developing an unsupervised neural network autoencoder to extract telluric transmission spectra from solar spectra, achieving similar accuracy to synthetic methods with less computational expense.
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 $\mathrm{H_2O}$ and $\mathrm{O_2}$ in the atmosphere of Earth. We demonstrate how the extracted components can be used to remove $\mathrm{H_2O}$ and $\mathrm{O_2}$ tellurics in a validation observation with similar accuracy and at less computational expense than a synthetic approach with molecfit.