Tau-REx I: A next generation retrieval code for exoplanetary atmospheres

TL;DR

TauRex is a comprehensive Bayesian retrieval framework for exoplanetary atmospheres that leverages advanced algorithms and molecular data to improve atmospheric characterization from spectroscopic data.

Contribution

It introduces a novel, fully Bayesian, line-by-line retrieval code with optimized molecular data use, unbiased priors, and scalable algorithms for exoplanet atmosphere analysis.

Findings

Demonstrated effective parameter retrieval on a theoretical hot-Jupiter spectrum.

Showed how Signal-to-Noise and spectral resolution affect parameter retrievability.

Validated the framework's ability for model selection and large parameter space mapping.

Abstract

Spectroscopy of exoplanetary atmospheres has become a well established method for the characterisation of extrasolar planets. We here present a novel inverse retrieval code for exoplanetary atmospheres. TauRex (Tau Retrieval for Exoplanets) is a line-by-line radiative transfer fully Bayesian retrieval framework. TauRex includes the following features: 1) the optimised use of molecular line-lists from the Exomol project; 2) an unbiased atmospheric composition prior selection, through custom built pattern recognition software; 3) the use of two independent algorithms to fully sample the Bayesian likelihood space: nested sampling as well as a more classical Markov Chain Monte Carlo approach; 4) iterative Bayesian parameter and model selection using the full Bayesian Evidence as well as the Savage-Dickey Ratio for nested models, and 5) the ability to fully map very large parameter spaces through optimal code parallelisation and scalability to cluster computing. In this publication we outline the TauRex framework and demonstrate, using a theoretical hot-Jupiter transmission spectrum, the parameter retrieval and model selection. We investigate the impact of Signal-to-Noise and spectral resolution on the retrievability of individual model parameters, both in terms of error bars on the temperature and molecular mixing ratios as well as its effect on the model's global Bayesian evidence.