Segmentation of spectroscopic images of the low solar atmosphere by the Self Organizing Map technique

Abstract

We describe the application of Semantic Segmentation by using the Self Organizing Map technique to an high spatial and spectral resolution dataset acquired along the H$\alpha$ 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 analyzing 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.

published in MNARS

References in corpus (6)

• A Comparison of Flare Forecasting Methods, I: Results from the "All-Clear" Workshop
• Homologous white light solar flares driven by photosperic shear motions
• Observational Evidence of a Flux Rope within a Sunspot Umbra
• Observation of a 3D magnetic null point
• Restoring process of sunspot penumbra
• Real-time solar image classification: assessing spectral, pixel-based approaches

Cited by in corpus (2)

• IBIS-A: The IBIS data Archive. High resolution observations of the solar photosphere and chromosphere with contextual data
• YOUNG Star detrending for Transiting Exoplanet Recovery (YOUNGSTER) II: Using Self-Organising Maps to explore young star variability in Sectors 1-13 of TESS data