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Machine Learning and Data Mining for Automatic Detection and Interpretation of Solar Events.
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| We propose to use novel information technology (IT) methods, characteristic of machine learning and data mining, to address issues related to space science data. In particular, we will develop the capability to automatically detect and characterize solar events for purposes related to space weather. The detection and tracking of solar events is important for the Sun-Earth Connection study and space weather prediction. As the amount of data available becomes overwhelming and near-real time detection is important, there is a strong need to automate this process. The specific tasks addressed by this proposal are to use techniques already developed for other problems to automate the detection, tracking and characterization of solar events and to uncover meaningful relationships between different solar events. Towards that end we propose to develop automated and interdisciplinary computational means driven by machine learning, data mining, image processing, pattern recognition and computer vision. The main modules for our automated system include: (A) preprocessing to improve the quality of data using morphology and relaxation tools, (B) detection of solar objects using a combination of methods including boundary detection, region growing and wavelet analysis, (C) tracking using Condensation (Conditional Density Propagation) methods, (D) characterization of solar event, and (E) spatial/temporal data mining of associations between different type of solar events. The proposed methodology is iterative and the steps (ìmodulesî) reinforce each other in their findings. We will develop the software tools needed and test the proposed system using, among others, CME observations from LASCO (Large Angle Spectrometer and Coronagraph) and coronal observations from EIT (Extreme-ultraviolet Imaging Telescope) instruments on the SOHO spacecraft. This proposal investigates novel information technology and computational methods to increase productivity of the OSS missions and programs. It will be achieved through close collaboration between experts in computational sciences, computer science and space science. |
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