1 Machine Learning and Data Mining for Automatic Detection and Interpretation of Solar Events Jie Zhang (Presenting, Co-I, SCS*) Art Poland (PI, SCS*) Harry Wechsler (Co-I, Computer Science) Kirk Borne (Co-I, SCS*) Oscar Olmedo (student, SCS*) (George Mason University) * SCS: School of Computational Sciences at GMU NASA AISRP NASA AMES, Moffett Field, CA April 4 – 6, 2005

2 Introduction Why solar events? Great interests of scientific understanding Great interests of practical use: the space weather What are solar events? Examples CME FLARE Dimming (coronal mass ejection)

3 Year 1996 – 2004 Flare Count CME Count 8852 Daily Min Max (1996) (2002) Flare CME Sunspot CME/Flare Statistics

4 Objectives Our main objective is to develop an automatic system for CME detection, tracking, characterization and source region location An automatic system is needed Timely detection, necessary for space weather forecasting Objective characterization, removing human bias Reducing human cost Data volume and number of events are enormous Explosively growth of data (SOHO, STEREO and SDO)

5 Methods Image Processing (current work) Pre-processing Detection and Tracking Characterization Machine Learning (future work) Develop robust and efficient algorithms for event detection Learning Methods Statistical learning theory, e.g., Support Vector Machine (SVM) Performance Evaluation Benchmark (catalog by human) ROC (Receiver Operating Characteristic) curve: hit, miss, or false-detection Data Mining (future work) Association of events from different sets of observations Space, and Time Physical parameters, e.g., intensity

6 Image Properties Find a faint moving object against a strong slow- varying background CME, like other astrophysical objects, is optically thin; no hard surface An object without fixed shape; an expansion flow

7 Image Processing: Pre-processing Calibration Filtering and Smoothing Differencing Polar Transformation

8 Image Processing: Initial Detection Finding CME angular expansion Projection Threshold : get core angles Morphology analysis Region Growing Closing (Dilation + Erosion): join features with narrow gaps Opening (Erosion + Dilation): remove narrow features Finding CME Height Thresholding on the area of selected angular expansion Projection along the height

9 Image Processing: a Demo 2002/12/01 – 12/07: 431 images

10 Image Processing: Detection and Tracking After the first detection Set the time stamp, expire after 5 hours set the targeted tracking region Targeted-tracking reduces false detection significantly, e.g., remove contamination of CME trailing outflow Cleaning Remove sporadic detection Preliminary Statistics: 2002/12/01 – 2002/12/07 19 CMEs in human catalog 19 CMEs in machine catalog (25 before cleaning) hit: 14 (74%) miss: 5 (26%) false detection: 5 (26%)

11 Future Plan of this Project We are only a few months into this project, which is supported for only one year We are seeking a full 3-year funding to fulfill the proposed objectives Finish all image processing tasks C2 (almost done) C3 (under development) EIT (under development) Use machine learning methods to develop robust algorithms (future) Use data mining methods to integrate detections, for the ultimate goal of space weather prediction (future) Make a computer-generated event catalog

12 The Future Automatic detection of all relevant events in the integrated Sun-Earth connection system Sun Solar Corona Heliosphere Magnetosphere Ionosphere Virtual X Observatories: contributor and user Machine learning and Data Mining for general science discovery