The inversion of Stokes profiles with artificial neural networks

Abstract

We investigate the application of artificial neural networks (ANNs) for the interpretation of Stokes profiles. We have employed ANNs to approximate the nonlinear inverse mapping between the Stokes profiles and some of the underlying atmospheric parameters. This approximate model is used in the following to carry out a fast non-iterative inversion of synthetic Stokes profiles. We have used synthetic Stokes profiles of the photospheric infrared line Fe I λ15648 to demonstrate that the ANNs are capable to yield accurate single valued estimates of the complete magnetic field vector, line-of-sight (LOS) velocity, microturbulence, macroturbulence and the filling factor with exceptional speed. For a stratified atmosphere we also demonstrate that these single valued parameters do represent very good averaged values of the input stratification. To retrieve some of the temperature information encoded in the Stokes profiles we modeled a neural network classifier on the basis of several semi-empirical model atmospheres (i.e. temperature and pressure stratification). With this classifier we are able to determine the probability that a given Stokes profile has its origin from a particular temperature stratification of a semi-empirical model.