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Feature Detecting: Solar Magnetic Tracking by SWAMIS

Feature Detecting: Solar Magnetic Tracking by SWAMIS. Xin Chen. Introduction. Paper review Solar Magnetic Tracking I~III. D.A.Lamb in C.E.Deforest’s group at SwRI. Apply to our observation Oct. 2011, Hinode NFI Looking for magnetic cancellation.

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Feature Detecting: Solar Magnetic Tracking by SWAMIS

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  1. Feature Detecting: Solar Magnetic Tracking by SWAMIS Xin Chen

  2. Introduction • Paper review Solar Magnetic Tracking I~III. D.A.Lamb in C.E.Deforest’s group at SwRI. • Apply to our observation Oct. 2011, Hinode NFI Looking for magnetic cancellation

  3. Solar Magnetic Tracking. I. Software Comparison and Recommended Practices DeForest, C. E., Hagenaar, H. J., Lamb, D. A., Parnell, C. E. & Welsch, B. T. ApJ, 2007 The Southwest Automatic Magnetic Identification Suite -- SWAMIS

  4. Tracking Algorithms • Preprocessing • Discrimination • Feature Identification • Feature Association • Filtering Based on Size/Longevity • Classification of Origin and Demise

  5. Tracking Algorithms Preprocessing: Reduce noise & eliminate perspective effects • Temporal averaging • Projection angle scaling • Resampling (spatial) • Despike

  6. Tracking Algorithms Discrimination: (In pixels) • Duel threshold: High ->isolated pixels Low ->adjacent to already selected Can be both spatial & temporal • Selecting like “contagion” • Minimum size & lifetime requirements

  7. Tracking Algorithms Feature Identification (In each frame) • Direct “Clumping” • Gradient based “downhill” The tradeoff, depends on your objective.

  8. Fig. 1.—Effect of different feature-identification schemes on the identified structure of a large flux concentration. (A) Clumping identifies all connected above threshold pixels into a single feature. (B) Downhill methods identify one feature per local maximum region. (C) Curvature methods identify the convex core around each local maximum.

  9. Tracking Algorithms Feature Association (In adjacent frames) • Duel-maximum-overlap criterion, (Flux & area -> flux weighted sense) Filtering Based on Sized/Longevity

  10. Tracking Algorithms Fig. 2.—Pathological association case. Features A and B are in the previous frame, C and D in the current frame. A maximum-overlap method associates B and C. The recommended associative algorithm (dual-maximum overlap) associates B = C if and only if B ∩ C is the largest of C’s intersecting regions and also the largest of B’s intersecting regions. A and B merge to form C, at the same time that D calves via fragmentation from B.

  11. Tracking Algorithms Classification of Origin & Demise (Evolution) • Appearance / Disappearance • Emergence / Cancellation • Fragmentation / Merger

  12. Solar Magnetic Tracking. II. The Apparent Unipolar Origin of Quiet‐Sun Flux Lamb, D. A., DeForest, C. E., Hagenaar, H. J., Parnell, C. E. & Welsch, B. T. ApJ, 2008

  13. Solar Magnetic Tracking. III. Apparent Unipolar Flux Emergence in High-Resolution Observations Lamb, D. A., DeForest, C. E., Hagenaar, H. J., Parnell, C. E. & Welsch, B. T. ApJ, 2010

  14. Unipole Appearance Clear example of convergence causing an MDI Appearance. The dashed circle highlights the region of interest. The blue, cyan, and magenta features begin moving toward each other. By the time of panel (c), the flux of the three previously distinct features is close enough to rise above the detection threshold in MDI and cause an Appearance (solid black outline in panel (c)). The frames are separated by 5 minutes.

  15. Our Observation • Coordinated observation between NSO/IBIS & Hinode • Magnetogram from NFI • Oct. 20, 2011, Coronal hole • 4 hour statistical analysis

  16. Target Region White box: NFI FOV at 16:00 UT

  17. Preprocessing • Offset compensate • Missing data • Alignment • Spatial smooth • Temporal smooth

  18. Alignment • Feature detecting is very sensitive to alignment. • Long period alignment -- 4 hours. • Using averaged magnetogram. • Special shift at bad frame.

  19. Running SWAMIS • Threshold: low-> 15G, high -> 30 G • Detecting method: clumping • Minimum size in a single frame: 4 pixels • Minimum lifetime: 3 frames • Minimum size for each feature: 9 pixels (Movie external, no link)

  20. Statistical Analysis -1: UNKNOWN OR ERROR 0: SURVIVAL 1: APPEARANCE/DISAPPEARANCE 2: EMERGENCE/CANCELLATION 3: FRAGMENTATION/MERGER 4: COMPLEX

  21. Thank you! Next step: Compare with different regions, like quiet sun, etc. Match location with On-disk type II spicules (IBIS). Link of SWAMIS: http://www.boulder.swri.edu/swamis/ Reference: Lamb, D. A., DeForest, C. E., Hagenaar, H. J., Parnell, C. E., Welsch, B. T., ApJ, 2010; 720(2):1405 Lamb, D. A., DeForest, C. E., Hagenaar, H. J., Parnell, C. E., Welsch, B. T., ApJ, 2008; 674(1):520 DeForest, C. E., Hagenaar, H. J., Lamb, D. A., Parnell, C. E., Welsch, B. T., ApJ, 2007; 666(1):576 Wang H, Johannesson A, Stage M, Lee C, Zirin H., Sol. Phys.,1998;178:55

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