Toward Atmospheric Retrievals of Panchromatic Light Curves: ExPLOR-ing Generalized Inversion Techniques for Transiting Exoplanets with JWST and Ariel

Abstract

Conventional atmospheric retrieval codes are designed to extract information, such as chemical abundances, thermal structures, and cloud properties, from fully “reduced” spectra obtained during transit or eclipse. Reduced spectra, however, are assembled by fitting a series of simplified light curves to time-series observations, wavelength by wavelength. Thus, spectra are postprocessed summary statistics of the original data, which by definition do not encode all the available information (i.e., astrophysical signal, model covariance, and instrumental noise). Here, we explore an alternative inversion strategy where the atmospheric retrieval is performed on the light curve directly, i.e., closer to the data. This method is implemented in EXoplanet Panchromatic Light curve Observation and Retrieval (E x PLOR), a novel atmospheric retrieval code inheriting from the T au RE x project. By explicitly considering time in the model, E x PLOR naturally handles transits, eclipses, phase curves, and other complex geometries for transiting exoplanets. In this paper, we have validated this new technique by inverting simulated panchromatic light curves. The model was tested on realistic simulations of a WASP-43 b-like exoplanet as observed with the James Webb Space Telescope (JWST) and Ariel telescope. By comparing our panchromatic light-curve approach against conventional spectral retrievals on mock scenarios, we have identified key breaking points in information and noise propagation when employing past literature techniques. Throughout the paper, we discuss the importance of developing “closer-to-data” approaches such as the method presented in this work, and highlight the inevitable increase in model complexity and computing requirements associated with the recent JWST revolution.

Keywords

• Exoplanet atmospheres
• Astronomy data analysis
• James Webb Space Telescope
• Time series analysis