An automated detection of sunspots on the Ca II K1 and SOHO/MDI images

1. An automated detection of sunspots on the Ca II K1 and SOHO/MDI images S. Zharkov, V.V. Zharkova, A.K. Benkhalil, S.S. Ipson Bradford University, UK EGSO Solar Feature Catalogues (SFCs) http://www.cyber.brad.ac.uk/egso/

2. Presentation plan • The observations and data reduction • Sunspot Detection • Detection on the SOHO/MDI white light images • Detection on the Meudon Ca II KI images • Parameters Extracted • Magnetic Parameters (correlation w/ MDI Mg) • Statistical analysis of the sunspots-magnetic flux correlation • Sunspots tracking - movies • Conclusions

3. Observations • Full disk solar photo intensitygrams, April-July 2002 • SOHO/MDI white light images (4 per day) • Meudon Ca II K1 (3934 A) images, April, July 2002 • SOHO/MDI full disk magnetograms (15 per day) – Ni I 6767A

4. Pre-processing techniques(Zharkova et al., 2003, Solar Phys., accepted) Difficulties with images: • Errors in FITS header information • Image shape (ellipse), centre and the pole coordinates • Weather transparency (clouds) and different thickness of atmosphere • Centre-to-limb darkening • Defects in data (strips, lines, intensity)

5. SUNSPOTS Image Pre-processing Original image Flat image Thresholding: results depend on choice of the threshold – Use Edge Detection instead

6. Automated Detection in White Light images • Gaussian Smoothing is applied to a pre-processed flat image with optional contrast stretching, followed by edge-detection (Sobel, Morph_Gradient) • The resulting image is much less sensitive to threshold choice. • Remove the limb edge, and use morphological operations to filter the noise and obtain a local region possibly containing sunspot or sunspots. • Exaamine each region, find sunspots using region’s statistical properties and Quiet Sun int. value to determine threshold value. • Examine each sunspot, do segmentation of umbra/ penumbra (QSun int, stats)

7. Automated Detection in White Light images Gaussian Smoothing, Sobel edge detector and threshold

8. Automated Detection in White Light images Remove the limb edge, and use morphological operations to filter the noise and obtain a local neighbourhood possibly containing a sunspot.

9. SUNSPOTSThresholding and Region Growing Locally Each region is examined, seeds are found by thresholding, with the thresholding Value determined as a function of the region statistical properties and quiet Sun intensity value

10. Sunspots detection on SOHO/MDI images • = a) The original cleaned image b) The detection edges c) The regions of interests after filtering d) The detection results e) The close-up of sunspot groups with umbra (black) and penumbra (white)

11. SUNSPOT DETECTION on Ca II K1 line images Original image from the Meudon Observatory, 02/04/02 Processed

12. SUNSPOT DETECTION on Ca II K1 line images Processed with some contrast stretch to emphasise the problems Detection Results

13. Automated Detection in Ca II KI images • Similar to White Light (but different parameters) • Results are then classified based on detected regions’ statistics, location on the disk, size, principal coeff and Quiet Sun Intensity • Subsequent classification is performed by looking at sunspot candidates positions in relation to each other • Results are satisfactory at best, however, can be improved if a sequence of images is considered instead of a single one.

14. SUNSPOT DETECTION on Ca II K1 line images Pre processed image from the Meudon Observatory, 04/04/02 Detection Results

15. a) The original image cleaned b) The detected edges c) The found regions (dilated) image d) The final detection results superimposed on original image The extract from d) Sunspot Detection on the CaII K1 line full disk images ( Meudon Observatory)

16. Sunspot DB • Each detected sunspot is stored as a raster scan of a bounding rectangle extracted from the image where the detection was made • segmenting quiet sun, umbral and penumbral pixels. • Chain Code also considered for larger sunspots • IDL code will be provided for feature reconstruction

17. Parameters Extracted • Observational • Observatory, Instrument, Wavelength, Date, Carrington Rotation etc • Processing • Disk Center, Radius, Scale (arcsec/pixel), Quiet Sun Intensity • Also FR and Cleaning code parameters • Including date run, version, Institute

18. Parameters Extracted • Sunspot Parameters • Gravity Center Coordinates • Carrington & Arcseconds • Size (Helio & PixelCount) • Diameter (Degrees) • Mean Intensity Ratio • # umbras detected • Umbra Size (pixels) – (degrees estimate?) • Max & Min Intensity • Bounding Rectangle

19. Parameters Extracted • Sunspot Parameters (considered) • Principal Coefficients (as shape indicator) • Magnetic • Total Magnetic Flux • Max Magnetic Flux • Sunspot Polarity (??) Red – positive magnetic field up to 1500 G Blue – negative magnetic field down to -1500 G White – magnetic field in range -20 G to +20G

20. SUNSPOTSDetection Results And MDI Magnetogram

21. MDI magnetograms and sunspots • MDI magnetogram pixels less than -500 G and greater than +500 G superimposed on MDI white light image • Images are syncronised • By looking at magnetograms, sunspot polarity can be determined • Also using the Active Region information, sunspots can be classified into groups (in progress)

22. Sunspots – magnetic flux correlation in April ‘02 • Total sunspots detected – 4242 • Total s/s selected in (500x500) area -2520 • Defined sunspot and umbra area • Extracted magnetic flux from s/s and umbra • Extracted maximum magnetic flux (s/s + u) • Plotted s/s areas versus total magnetic flux • Investigate the south-north asymmetry • Tracked a few sunspots during this month

23. Positive Bs= AxS + D 40576.2,-1579.2  r=0.987 r=0.982 Negative Bs= AxS + D 41549.0, -1519.0 

24. Correlation: r=0.985 Total magnetic flux – sunspot area Bs= AxS + D Full disk fit: 40895.9,-1461.9 r=0.981 r=0.990 South 41821.1,-1480.5 North 40405.3,-1427.9

25. Bu= CxU + E Full disk  71894.7, -1298.6 North 75710.8, -2232.6 r=0.935 South 65071.2, -60.6 Total umbra magnetic flux – umbra area r=0.936 r=0.936

26. 1042.0, 673.6 r=0.862 1445.7, 217.7 r=0.862 Max magnetic flux – umbra area/diameter Bm= Fx√U + K(top left )Bm= Fx4√U + K(top right) r=0.879 r=0.829 South: 1014.8, 676.7 North: 1053.8, 672.7

27. Temporal evolution of sunspots|B| – solid line, Bmax – dotted, sunspot area – dashed, umbra area – dot-dashed Sunspot 0: 1-4/04 Sunspot 1: 1-5/04 Sunspot 2: 1-5/04 Sunspot 3: 1-5/04

28. Temporal evolution of sunspotsSunspot 1: 1-5 April ‘02

29. Temporal evolution of sunspotsSunspot 2: 1-5 April ‘02

30. Temporal evolution of sunspotsSunspot 3 : 1-5 April ‘02

31. Current Issues • Sunspot Group Definition • Wolf Numbers • Necessary for classification • Sunspot Pores • How to distinguish from noise • False Detection Rates for Ca II k1 • (We think it) can be avoided if sequence of observations is considered for verification

32. Conclusions • Robust stable techniques for automated detection of sunspots on the space and ground-based solar images are presented. • Solar Feature Catalogue online – start 2004 • Combining detection results for Sunspots and Active Region may allow for solving classification problem • 466 white light observations (April-July 2002) processed • Additional information can be extracted from MDI magnetograms thus allowing us to consider sunspot polarity • The synoptic map of detected sunspots in April 02 were compared with those done manually in the Meudon observatory that shown a good accuracy of detection

33. SUNSPOTSThe Future Plans • To process large amounts of white light and Ca II k1 line images, build the synoptic maps and extract the data such as sunspot numbers, sunspot area etc  Feature Catalogue • We are looking at correlating this data with other data such as Faculae/Active Regions, Magnetic Fields, Solar Flares etc • Investigating the North-South Hemisphere Sunspot Asymmetry

34. Questions, please…

35. Table 1. The accuracy of sunspot detection and classification for CaII K1 (see the text for description)

36. Parameters Extracted