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

TL;DR

Exo Skryer is a scalable, JAX-accelerated atmospheric retrieval framework for exoplanets and brown dwarfs, enabling efficient analysis of large spectral datasets and complex models.

Contribution

It introduces Exo Skryer, a novel retrieval framework using JAX for scalable, efficient modeling and a new method to directly retrieve optical constants of aerosols.

Findings

Consistent retrieval results with previous efforts for JWST data.

Demonstrated efficiency of Exo Skryer for complex, high-dimensional models.

Validated new method for retrieving aerosol optical constants.

Abstract

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 to 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.