Development of a Deep Learning Model for Inversion of Rotational Coronagraphic Images Into 3D Electron Density

Abstract

We present, for the first time, a deep learning model that returns the three-dimensional (3D) coronal electron density from coronagraphic images. The intensity of coronagraphic observations arises from the Thomson scattering of photospheric light by the coronal electrons. We use MHD numerical simulations to obtain realistic 3D electron density and construct error-free training sets consisting of input (observation) and target (electron density) images. In the training sets, the input images are directly synthesized from the target 3D electron density by applying the Thomson scattering theory. The input and target images are in the form of latitude-longitude maps given at a radius, often referred to as synoptic maps. Using synoptic maps reduces a tomographic method to an image translation problem. We use pix2pixHD, one of the well-established supervised image translation methods and develop models for six selected heights: 2.0, 2.2, 2.5, 4.0, 6.0, and 12.0 solar radii. All six models have similar performance and the mean absolute percent error of the generated density images is less than 7% with respect to the ground-truth simulated data sets.