Segmentation of spectroscopic images of the low solar atmosphere by the self-organizing map technique

We describe the application of semantic segmentation by using the self-organizing map technique to an high spatial and spectral resolution data set acquired along the Ha line at 656.28 nm by the Interferometric Bi-dimensional Spectrometer installed at the focus plane of the Dunn solar telescope. This machine learning approach allowed us to identify several features corresponding to the main structures of the solar photosphere and chromosphere. The obtained results show the capability and flexibility of this method to identifying and analysing the fine structures which characterize the solar activity in the low atmosphere. This is a first successful application of the SOM technique to astrophysical data sets.

Automated summary

This study applies semantic segmentation using the self-organizing map technique to a high-resolution data set, successfully identifying and analyzing fine structures of solar activity, marking the first successful application of the SOM technique to astrophysical data sets.