Measuring solar magnetic fields with artificial neural networks

Abstract

The quantification of the solar magnetic field is a crucial step in modern solar physics to understand the dynamics, activity and variability of our star. Presently, a reliable inference of these fields is only possible by means of a computer-intensive process that has so far limited scientists to the analysis of observations from small regions of the solar disk, and/or very crude spatial and temporal resolution. This work presents a different approach to the problem, in which a multilayer perceptron, trained with known synthetic profiles, is able to recognize the profiles and return the magnetic field used to synthesize them. The network is then confronted with real observations of a sunspot which had been previously inverted using traditional inversion techniques. A quantitative comparison between these two procedures shows the reliability of the network when applied to points having magnetic filling factors larger than approximately 70%. The dramatic decrease in the required computing time presents an opportunity for the routine analysis of large-scale, high-resolution solar observations.