Detecting Thermal Features in Massive Streams of Solar Images

• Activity Number: 433
• Type: Invited
• Date/Time: Wednesday, August 6, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistical Learning and Data Mining
• Author(s): Nathan M. Stein*+
• Companies: University of Pennsylvania
• Keywords: Image processing ; Multiband imaging ; Solar astronomy

Abstract

Telescopes such as the Atmospheric Imaging Assembly for the Solar Dynamics Observatory produce massive streams of high-resolution multiband images of the Sun. Automatically identifying images or features for scientific follow-up is crucial if the data from these telescopes are to be fully utilized. Unfortunately, the available spectral information in AIA images is extremely limited, making it difficult to recover thermal properties of different regions on the Sun and identify regions with interesting thermal characteristics. Because of the quantity of data, model-based reconstruction of thermal properties from these images can be too computationally expensive to apply to full-resolution images. We will discuss approaches for identifying image regions with similar thermal properties, with the goal of closely approximating the results of expensive model-based methods, under computational constraints.