Exploring the Sun’s upper atmosphere with neural networks: Reversed patterns and the hot wall effect

Abstract

We have developed an inversion procedure designed for high-resolution solar spectro-polarimeters, such as those of Hinode and the DKIST. The procedure is based on artificial neural networks trained with profiles generated from random atmospheric stratifications for a high generalization capability. When applied to Hinode data, we find a hot fine-scale network structure whose morphology changes with height. In the middle layers, this network resembles what is observed in G-band filtergrams, but it is not identical. Surprisingly, the temperature enhancements in the middle and upper photosphere have a reversed pattern. Hot pixels in the middle photosphere, possibly associated with small-scale magnetic elements, appear cool at the log τ500 = −3 and −4 level, and vice versa. Finally, we find hot arcs on the limb side of magnetic pores. We interpret them as the first piece of direct observational evidence of the “hot wall” effect, which is a prediction of theoretical models from the 1970’s.