To Sample or Not to Sample: Retrieving Exoplanetary Spectra with Variational Inference and Normalizing Flows

Abstract

Current endeavours in exoplanet characterization rely on atmospheric retrieval to quantify crucial physical properties of remote exoplanets from observations. However, the scalability and efficiency of said technique are under strain with increasing spectroscopic resolution and forward model complexity. The situation has become more acute with the recent launch of the James Webb Space Telescope and other upcoming missions. Recent advances in machine learning provide optimization-based variational inference as an alternative approach to perform approximate Bayesian posterior inference. In this investigation we developed a normalizing-flow-based neural network, combined with our newly developed differentiable forward model, Diff-τ, to perform Bayesian inference in the context of atmospheric retrievals. Using examples from real and simulated spectroscopic data, we demonstrate the advantages of our proposed framework: (1) training our neural network does not require a large precomputed training set and can be trained with only a single observation; (2) it produces high-fidelity posterior distributions in excellent agreement with sampling-based retrievals; (3) it requires up to 75% fewer forward model calls to converge to the same result; and (4) this approach allows formal Bayesian model selection. We discuss the computational efficiencies of Diff-τ in relation to TauREx3's nominal forward model and provide a “lessons learned” account of developing radiative transfer models in differentiable languages. Our proposed framework contributes toward the latest development of neural network–powered atmospheric retrieval. Its flexibility and significant reduction in forward model calls required for convergence holds the potential to be an important addition to the retrieval tool box for large and complex data sets along with sampling-based approaches.

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

“…On one side, potential avenues to overcome those issues include the development of more physically motivated retrievals (i.e., with fewer degrees of freedom) based on our continuously evolving understanding of exo-atmospheres and instruments. On the other side, novel inference techniques, either based on more scalable samplingbased methods-such as dynamic nested sampling (Higson et al 2019) or Phantom-powered nested sampling (Albert 2023) -or alternative techniques-such as variational inference (Yip et al 2024), simulation-based inference (Gebhard et al 2023;Vasist et al 2023) or machine-learning-accelerated surrogate modeling (Himes et al 2022)-need to be developed.…”