Telluric Correction of M-dwarf Stars using Machine Learning

Abstract

The study of M-dwarf stars is of prime scientific interest to us because of their closer habitable zones and the favorable conditions they offer for exoplanet detection. However, telluric contamination of the ground-based spectra results in sharp absorption lines, which makes their study cumbersome. Removing this contamination is necessary for estimating key stellar parameters. The central contribution is a one-dimensional Convolutional Neural Network (CNN) that retrieves the four atmospheric parameters governing telluric absorption: pressure, temperature, humidity, and airmass. These predicted parameters are passed to Telfit which produces an estimated telluric spectrum. The observed spectrum is then divided by this estimated telluric spectrum to obtain the telluric corrected spectrum. As this network is trained exclusively on synthetic spectra, a domain gap exists at inference. Two domain-adaptation strategies are evaluated: a CycleGAN following the Cycle-StarNet framework for explicit synthetic-to-real translation and a Domain-Adversarial Autoencoder (DAAE) that learns domain-invariant spectral representations. The Domain-Adversarial Autoencoder (DAAE) achieves the lowest loss against a telluric corrected CARMENES reference spectrum outperforming all other model variants. The CycleGAN fails due to discriminator collapse under severe class imbalance between the number of real and synthetic spectra.