Mapping the Sun's upper photosphere with artificial neural networks

TL;DR

This paper introduces a neural network-based inversion method for high-resolution solar spectro-polarimetric data, revealing detailed temperature structures and the hot wall effect in the Sun's upper photosphere.

Contribution

The authors develop a neural network inversion technique tailored for solar spectro-polarimeters, enabling detailed mapping of temperature and magnetic features in the Sun's upper photosphere.

Findings

Identified a hot fine-scale network structure with height-dependent morphology.

Discovered reversed temperature enhancement patterns in the middle and upper photosphere.

Provided observational evidence of the hot wall effect near magnetic pores.

Abstract

We have developed an inversion procedure designed for high-resolution solar spectro-polarimeters, such as Hinode/SP or DKIST/ViSP. 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 to small-scale magnetic elements, appear cool at the log(tau_500)=-3 and -4 level, and viceversa. Finally, we find hot arcs on the limb side of magnetic pores, which we interpret as the first direct observational evidence of the "hot wall" effect in temperature.