Mining Coronal Loops in Solar Images from the SOHO collection

Abstract

We present our preliminary findings as part of a new data mining application aiming at the automatic detection of images with coronal loops from one of NASA's solar image databases, known as EIT. Coronal loops are immense arches of hot gas on the surface of the Sun, thought to be jets of hot plasma flowing along in the alleys between the strong coronal magnetic fields. We use various data mining techniques including combining crisp and fuzzy classifiers for automated detection of blocks extracted from EIT solar images. Our data mining and retrieval system helps provide relevant data to astrophysicists who need such data to study the solar corona, and whose work is traditionally hindered by the need to manually sift through thousands of images in order to locate the very few that are useful for further analysis. Our data-driven approach is distinct from related image processing based approaches that cannot scale to large image databases because they rely mostly on semi-automated detection and on heavy and computationally intensive local shape analysis.