Unsupervised Spectral Unmixing For Telluric Correction Using A Neural Network Autoencoder

Line H Clemmensen, Rune Dodensig Kjærsgaard

Abstract:

The absorption of light by molecules in the atmosphere of Earth is a complication
for ground-based observations of astrophysical objects. Comprehensive informa-
tion 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.