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An Automatic System for CME Detection and Source Region Identification

Solar and Space Physics Virtual Observatories Conferences Oct. 27 – 29, 2004 Greenbelt, MD. An Automatic System for CME Detection and Source Region Identification. Jie Zhang (jiez@scs.gmu.edu) Art Poland, Harry Wechsler

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An Automatic System for CME Detection and Source Region Identification

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  1. Solar and Space Physics Virtual Observatories Conferences Oct. 27 – 29, 2004 Greenbelt, MD An Automatic System for CME Detectionand Source Region Identification Jie Zhang (jiez@scs.gmu.edu) Art Poland, Harry Wechsler Kirk Borne George Mason University

  2. Introduction • CME is the major driven force of severe space weather that have technology and societal impacts • An automatic event detection system is needed, because • Timely detection of events, which is crucial for space weather forecasting • Reducing human cost, overcoming the limitation of human performance; growing amount of data, e.g., SOHO, STEREO and SDO • Objective event characterization, by imposing a uniform event processing standard, providing consistent data for users • Flexibility and scalability, allowing further in-depth applications added on later.

  3. Three computational components • Image Processing • Event/pattern recognition • Machine Learning • Developing robust and efficient image analysis and pattern recognition algorithms • Statistical Learning Theory (SLT), e.g., Support Vector Machine (SVM) • Transductive Inference, locality aspect of objects • Data Mining • Case Based Learning (CBL) • Memory-based Reasoning (MBR)

  4. Six major tasks in the system T5: Associate CME events with dimming events T6: Performance Evaluation and Enhancement, iterative task 1 to task 5

  5. CME Detection/Tracking and Characterization • Find a faint moving object against a cluttered background • CME, like other astrophysical objects, is optically thin; no hard surface • CME, no fixed shape, an expansion flow

  6. CME Detection/Tracking and Characterization • Preprocessing • Calibration • Filtering and relaxation • polar transformation • Detection • Morphology analysis • Boundary detection • Region Growing • Tracking • CONDENSATION (CONditaional DENSity propagATION) • Use temporal relations between frames

  7. EIT dimming Detection and Characterization • EIT or coronal dimming, the most reliable observations to locate CME disk source region • Characterization • Heliocentric coordinate • Timing • Size • Intensity

  8. Data Mining, CME Source Regions • Find out Spatial and Temporal association rule • CME • Timing • Position angle and size • Velocity • Coronal dimming • Timing • Heliocentric coordinate • Size and dimming intensity

  9. Performance Evaluation • Understanding the applicability of the proposed methods • Achieving the best performance for different needs • Building catalogs • Neal real time detection for forecasting • In depth research • Find true error rate from apparent error rate • ROC (Receiver Operator Characteristics) curve, tradeoff between false alarm and detection rate • Cross-validation • Bootstrapping

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