First Science Results from Solar Data Mining Using Automated Feature Detection

Wednesday, 17 December 2014: 5:45 PM
Petrus C Martens, Georgia State University, Physics & Astronomy, Atlanta, GA, United States
The SDO Feature Finding Team (FFT) has produced 16 automated feature tracking modules for data from SDO, LASCO, and ground-based H-alpha observatories. The metadata produced by those modules and others are available from the Heliophysics Events Knowledgebase (HEK) and the Virtual Solar Observatory (VSO). Having metadata available for large amounts of events and phenomena, obtained with consistent detection criteria unlike catalogs produced by human observers, allows researchers to effectively search solar data for patterns.

I will show a number of science results obtained recently. Not surprisingly several of the patterns are well known (e.g. flares occur mostly in active regions), but some really surprising new trends have been discovered as well, in at least one case upending scientific consensus. These results show the power and promise that systematic feature recognition and data mining holds for solar physics.