1. Coronal Boundaries of Active Regions Derived From Soft X-ray Images

2. Abstract Monitoring the fluctuations in EUV and soft X-ray coronal emission from active regions is useful for studies of active region behavior and evolution. To do so requires robust techniques for defining, recognizing and extracting active region boundaries from images recorded at these wavelengths.

3. The problem is more difficult than boundary definition for photospheric or chromospheric features for several reasons, including: 1. Coronal emission is extended in three dimensions rather than two, so that the boundary is, correspondingly, three dimensional as well. 2. Coronal emission is optically thin, which (together with the three dimensionality of the emission) results in confusion of foreground and background emission. 3. Coronal emission is highly variable, so the very definition of an active region boundary in the corona is to some degree imprecise. 4. Active regions frequently share sets of coronal loops, further confusing the specification of their boundaries.

4. GOES Full Disk X-ray Light Curves

5. Background of SolarSoft ‘Findstuff’ Package Originally developed for EIT He II 304 limb feature detection Originally developed for EIT He II 304 limb feature detection ‘Findstuff’ package developed to prepare arbitrary image sets, process them to determine ‘feature’ boundaries based upon a set of contour levels ‘Findstuff’ package developed to prepare arbitrary image sets, process them to determine ‘feature’ boundaries based upon a set of contour levels Construct a database which characterizes the features in each image (1 record per image) Construct a database which characterizes the features in each image (1 record per image) Optionally maintain ‘feature mask’ image set parallel to image set Optionally maintain ‘feature mask’ image set parallel to image set

6. Contents of Extracted Feature Database Center of brightness, radial and angular extent of each feature Center of brightness, radial and angular extent of each feature Brightness moment distribution for each feature Brightness moment distribution for each feature Region number Region number

7. Input He II 304 Image

8. Off Limb Regions Extracted

9. Regions Identified

10. Potential Usefulness to Active Region Studies Active region light curves for active region evolution and flare prediction Active region light curves for active region evolution and flare prediction Evolution of brightness kernels within active regions Evolution of brightness kernels within active regions Use of region mask images to compare coronal brightness evolution of active regions with brightness evolution at other wavelengths and temperatures, as well as spatial configuration evolution comparisons Use of region mask images to compare coronal brightness evolution of active regions with brightness evolution at other wavelengths and temperatures, as well as spatial configuration evolution comparisons For example, compare brightness kernel evolution in soft X-rays with photospheric magnetic field and current distribution evolution For example, compare brightness kernel evolution in soft X-rays with photospheric magnetic field and current distribution evolution Do this for systematically for all active regions for large scale studies Do this for systematically for all active regions for large scale studies

11. Required Enhancements For Extension To Feature Tracking To use the system for feature tracking, regions extracted from one image must be related to regions from proceeding and succeeding images To use the system for feature tracking, regions extracted from one image must be related to regions from proceeding and succeeding images Simplest approach used first – look for the ‘closest’ region in the proceeding image and associate Simplest approach used first – look for the ‘closest’ region in the proceeding image and associate If nearest prior region is too far away, call this region a new one. If nearest prior region is too far away, call this region a new one.

12. Test Case For Coronal Active Region Boundary Tracking GOES 12 SXI Imager chosen because: GOES 12 SXI Imager chosen because: –Full Disk –Continuous, reasonably high (~2 min) image cadence –Can be compared to GOES full sun X-ray monitors Application to AIA archives is clear Application to AIA archives is clear

13. Initial Image Set 29,000 images from September – November, 2001 29,000 images from September – November, 2001

14. SXI Image and Extracted Regions

15. Active Region 9690 Trace

16. Comparison of GOES full disk light curve with single active region light curve

17. Current Status Promising Promising Critical algorithms for linking regions from image to image have to be made more sophisticated and robust Critical algorithms for linking regions from image to image have to be made more sophisticated and robust Overlap of regions near limb is perhaps insoluble, but does not mean the regions extracted are useless (still show activity at particular sites) Overlap of regions near limb is perhaps insoluble, but does not mean the regions extracted are useless (still show activity at particular sites)

18. The End