TAU: A neural network based telluric correction framework

¹ Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads 324, 2800 Kgs. Lyngby, Denmark
e-mail: rdokj@dtu.dk
² DTU Space, National Space Institute, Technical University of Denmark, Elektrovej 328, 2800 Kgs. Lyngby, Denmark
³ Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA 91109, USA
⁴ Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA

Received: 14 April 2023
Accepted: 19 July 2023

Abstract

Context. Telluric correction is one of the critically important outstanding issues for extreme precision radial velocities and exoplanet atmosphere observations. Thorough removal of small so-called micro tellurics across the entire wavelength range of optical spectro-graphs is necessary in order to reach the extreme radial velocity precision required to detect Earth-analog exoplanets orbiting in the habitable zone of solar-type stars. Likewise, proper treatment of telluric absorption will be important for exoplanetary atmosphere observations with high-resolution spectrographs on future extremely large telescopes (ELTs).

Aims. In this work, we introduce the Telluric AUtoencoder (TAU). TAU is an accurate high-speed telluric correction framework built to extract the telluric spectrum with previously unobtained precision in a computationally efficient manner.

Methods. TAU is built on a neural network autoencoder trained to extract a highly detailed telluric transmission spectrum from a large set of high-precision observed solar spectra. We accomplished this by reducing the data into a compressed representation, allowing us to unveil the underlying solar spectrum and simultaneously uncover the different modes of variation in the observed spectra relating to the absorption from H₂O and O₂ in the atmosphere of Earth.

Results. We demonstrate the approach on data from the HARPS-N spectrograph and show how the extracted components can be scaled to remove H₂O and O₂ telluric contamination with improved accuracy and at a significantly lower computational expense than the current state of the art synthetic approach molecfit. We also demonstrate the capabilities of TAU to remove telluric contamination from observations of the ultra-hot Jupiter HAT-P-70b allowing for the retrieval of the atmospheric signal. We publish the extracted components and an open-source code base allowing scholars to apply TAU on their own data.