Exo Skryer: A JAX-accelerated sub-stellar atmospheric retrieval framework

Contemporary exoplanet and brown dwarf atmospheric research relies heavily on retrieval frameworks to recover thermal and chemical properties and perform model comparison in an observational data-driven approach. However, the computational effort required for retrieval modelling has rapidly increased, driven by JWST data that covers large spectral intervals at moderate spectral resolutions, and ground-based, high-resolution spectroscopy. To help tackle the computational burden faced by contemporary retrieval requirements, I present a new sub-stellar atmosphere retrieval modelling framework, Exo Skryer, that utilises the JAX library for Python to enable scalable, computationally efficient forward modelling as well as posterior sampling. I present example retrievals for pre- and current JWST era observations for both transmission and emission spectra, finding consistent results with previous retrieval modelling efforts, apart from a WASP-107b test case. In addition, I present a new method to directly retrieve the real and imaginary optical constants (\(n\), \(k\)) of suspected aerosol infrared absorption features. Due to its computational expediency, Exo Skryer} will be highly suited for future demanding retrieval efforts that incorporate more spatial dimensionality, complex forward models and high-dimensional parameter sets. Exo Skryer is available as open-source software on GitHub at this https URL