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Solar Loop Mining to Support Studies of the Coronal Heating Problem
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| The Coronal Heating Problem is the longest standing unsolved mystery in astrophysics. Measurements of the temperature distribution along the loop length can be used to support or eliminate various classes of coronal temperature models. The temperature analysis of coronal loops is a state-of-the-art astronomy. In order to make progress, scientific analysis requires data observed by instruments such as EIT, TRACE, and SXT . The combination of EIT, TRACE, and SXT information provides a powerful data set that will yield unprecedented detail on the plasma parameters of a variety of coronal loop structures. The biggest obstacle to completing this project is putting the data set together. EIT has taken over 300,000 images during its 6-year (and counting) mission. The search for interesting images (with coronal loops) is by far the most time consuming aspect of this project. Currently, this process is performed manually, and is therefore extremely tedious, and hinders the progress of science in this field. The next generation ''EIT'' called MAGRITE, scheduled for launch in a few years on NASA's Solar Dynamics Observatory, should be able to take 300,000 images in about four days! and will no doubt need state of the art techniques to sift through the massive data to support scientific discoveries. We propose an approach based on data mining to quickly sift through massive data sets downloaded from the online NASA solar image databases and automatically discover the rare but interesting images with solar loops, which are essential in studies of the Coronal Heating Problem. The proposed solar loop mining scheme will rely on the following components: 1) Collection and labeling of a sample data set of images coming from both categories (with and without solar loops), 2) An optimal feature selection strategy that will facilitate the retrieval task, 3) A classification strategy to classify the transformed image into the correct class, and 4) Appropriate measures to validate the effectiveness of the loop mining process. This project will be implemented in three main phases that target the image databases collected by two different instruments, EIT aboard the NASA/European Space Agency spacecraft SOHO and NASAís TRACE. We will leave open the possibility of targeting the SXT database on the Japanese Yohkoh spacecraft if time permits. Each phase will involve collecting and labeling a small sample of the images from the targeted instrument, for which a solar loop mining strategy is to be designed. All the results of this project: literature, software, and outputs Semantic loop features and class labels (in \emph{XML} format) of the developed classification methods on tested portions of the different instrument databases will be made available to the public and other interested researchers via the World Wide Web. |
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