Bayesian Stokes inversion with normalizing flows

• Published in: Astronomy and astrophysics (Berlin) 2022-03, Vol.659, p.A165
• Main Authors: Díaz Baso, C. J., Asensio Ramos, A., de la Cruz Rodríguez, J.
• Format: Article
• Language: English
• Subjects: Algorithms; Approximation; Bayesian analysis; Conditional probability; Inverse problems; Inversions; line: formation; Local thermodynamic equilibrium; Magnetic properties; Markov chains; Mathematical models; methods: data analysis; Monte Carlo simulation; Normalizing (statistics); Parameters; radiative transfer; Statistical inference; Stellar atmospheres; Sun: activity; Sun: atmosphere; Thermodynamics; Variational methods
• ISSN: 0004-6361; 1432-0746; 1432-0746
• DOI: 10.1051/0004-6361/202142018

Stokes inversion techniques are very powerful methods for obtaining information on the thermodynamic and magnetic properties of solar and stellar atmospheres. In recent years, highly sophisticated inversion codes have been developed that are now routinely applied to spectro-polarimetric observations. Most of these inversion codes are designed to find an optimum solution to the nonlinear inverse problem. However, to obtain the location of potentially multimodal cases (ambiguities), the degeneracies and the uncertainties of each parameter inferred from the inversions algorithms – such as Markov chain Monte Carlo (MCMC) – require evaluation of the likelihood of the model thousand of times and are computationally costly. Variational methods are a quick alternative to Monte Carlo methods, and approximate the posterior distribution by a parametrized distribution. In this study, we introduce a highly flexible variational inference method to perform fast Bayesian inference, known as normalizing flows. Normalizing flows are a set of invertible, differentiable, and parametric transformations that convert a simple distribution into an approximation of any other complex distribution. If the transformations are conditioned on observations, the normalizing flows can be trained to return Bayesian posterior probability estimates for any observation. We illustrate the ability of the method using a simple Milne-Eddington model and a complex non-local thermodynamic equilibrium (NLTE) inversion. The method is extremely general and other more complex forward models can be applied. The training procedure need only be performed once for a given prior parameter space and the resulting network can then generate samples describing the posterior distribution several orders of magnitude faster than existing techniques.