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Automatic detection of dynamic Ha dark features from high-cadence FMT observations

Develop an automatic program for identifying dynamic chromospheric dark features from high-cadence solar observations at varying Hα wavelengths. Explore the relationship between dark features and solar activities. Utilize the Hida FMT Catalogs for statistical analysis.

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Automatic detection of dynamic Ha dark features from high-cadence FMT observations

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  1. Automatic detection of dynamic Ha dark features from high-cadence FMT observations Speaker: Liu Yu @Kwasan and Hida Obs., Kyoto University

  2. Introduction • I present a fast program for automatic identification of dynamic chromospheric dark features from time series of full-disk solar images at three Ha wavelengths (center, and ±0.8Å) • It is a natural requirement for making such an automatic program since daily data is very huge in quantity (at least 1fpm)

  3. Introduction • Usually, one dark feature revealed from the off-band Ha observations may be correspondent to a surge, or enhancement of chromospheric network, or activity of a filament • The statistical work on them should facilitate the understanding of the triggers of small-scale reconnections, the coronal heating, and CMEs.

  4. Introduction • Particularly, the Ha surge activities have a very close relationship to both large-scale phenomena (SXR jets, flares), but also to small-scale bursts (Ellerman bombs, EUV blinkers) • The large-sample statistical analyses on surges is a quite different way from many previous work

  5. Hida FMT consists of six small telescopes, in which four ones obtain simultaneous full-disk data at different wavelengths. Hida/FMT

  6. Catalogs for FMT • http://www.kwasan.kyoto-u.ac.jp/observation/event/fmt/index_en.shtml • Since 1992 May, nearly 20,000 dark events have been registered with detailed classifications. They are valuable resource for further statistical analysis of surges and filaments. events with IB, L or M size marked • However, making these catalogs is an intensive work for a staff because he/she not only has to search for so many dark features that are usually very weak, but also needs to make a best decision on their classifications and other important parameters. • Therefore, a better way is needed

  7. A program for automatic surge detection for FMT Data including: • Hα • Hm━ -0.8Å from Hαcenter • Hp━ +0.8Å from Hαcenter • WL━ white-light continuum Data pro-processing: 1. Deleted poor-seeing data (Hm, WL) 2. Enhance the contrast (reduce limb darkening) 3. Possess more information (Making into standard FITS format)

  8. 1. Bad seeing judgment p(t) • For Hm data intensity Thick lines: poly-fittings, p(t) I - t I(t) frame sequence Bad seeing rule: I(t) < pI(t)*c, and n(t) > pn(t)*(2-c), or l(t) < pI(t)*0.5, or n(t) < pn(t)*0.1 percentage of noisy points Noise - t a noisy point is defined its intensity is lower than I(t) X c, where c=0.85

  9. For WL data I - t rule: I(t) < pI (t), or n(t) > pn (t) Noise - t

  10. 2. Limb darkening correction • consists two steps: • judge disk center and radius, using Sobel function for edge enhancement to obtain limb pixels. • make limb darken profile using a radial median filter and then with a second order polynomial fitting to the inner ¼ core. center-to-limb function: Limb ( r:r ) = median ( image ( r:r ) ) * a, where a = 0.6 Before reduction

  11. this step helps to enhance the intensity difference between the dark features with their background After reduction

  12. 3. PGM→FITS • After rotation and center correction, then transfer the original pgm data (Hm,Hp,Ha, WL) into fits type • The fits files Containuseful information,e.g., B0, r¤, data spatial resolution, date and time etc, which makes it convenient for the following processes speed: ~1s / frame (in my computer) for a typical observation day (8hrs): ~30 mins

  13. A flow chart for data pre-processing Mean I(t), N(t), profiles All hm / wl data Judge disc center, disc radius, B0 Fitting curves PI(t), PN(t) each hm / wl data B0 correction, center shift 0 Cloudy? no Produce limb darkening function and correct 1 yes Record this time Standard FITS file each ha / hp data 0 Same time? no 1 yes Cancel this frame

  14. Effective detection algorithm • The whole solar disc is evenlydivided into 109 box regions • In an Hm image, if one dark point group (cluster) whose intensity is lower than 90% that of the located box region, then it is defined as a surge candidate • For a series of Hm frames, if a same dark cluster appears frequently more than some threshold, then it is judged to be a real dynamic darkfeature • Time needed for an 8-hr observation: <90min, averagely

  15. N W adjustable Darkpoint group limb 250 (spot) 0 (out-disc) width: 39 pixels

  16. The choice of 90% intensity as an important parameter 01:15 90%

  17. A simple example The solar disk is evenly divide into 109 boxes. The sunspot pixels are valued with 250. The detection will be carried out in the inner circle for avoiding the noisy limb region.

  18. First, the procedure searches for dark points from every box in an Hm frame

  19. Checking if the darkpoints are in cluster, delete the isolated points. Two clusters have been detected. The procedure records the median center and length of the dark clusters.

  20. After then, the procedure identifies the real dark point clusters that appear for least two successive minutes according the recorded coordinates and times, and later coalesces the nearby clusters of the same time.

  21. For 1999-Aug-03, a part of the event catalog is made like ---

  22. An image containing four-wavelength observations

  23. Main Parameters • w : box size for dividing disc surface (19 pix) • di : intensity threshold (90%) • far : distance threshold for dark points from each other in a cluster(15 pix) • dd : size of JPEG images (35 pix) • min : minimum dark points in a cluster (3) • rad : distance threshold to attribute a dark point to a cluster (15 pix) • dt: threshold of time difference to coalesce two events of the same locations • theta : lat/long. confines for avoiding the noisy solar limb (65 deg) • leng : distance threshold for associating with sunspot and filaments(20 pix) • varian: threshold for convergence of dark point clusters (100, 80)

  24. Variance map Dark points Clusters in one frame Clusters in all frames Clusters after coalescence Basic processes in the procedure

  25. A flow chart for surge detection START: An Hm Fits image smooth Value in-disc darkpoint ¼ of median, out-disc 0 Weighted di (～90%±Δ) Divide the disc into 109 boxes, value those pixels from spot umbrae and other dark features 250 and 10, respectively Nearby WL image umbrae Obtain coordinates (x, y) of the10-value on-disk pixels N save median (x,y) into xy_step0.dat Y All Hm files finished? Possible Surges? N Y Continued next page

  26. final xy_step0.dat Cluster darkpoints with series number, Check group distance Merge, obtain all clusters (x, y) Check every record xy_step2.dat xy_step1.dat Appear at least two successive minutes Using Hm, Hp,time extension keep successive events Ha,Hp,Hm, Wl JPEG images S/F/N association Delete isolated records Mpeg movies xy_core.dat Merge, ∆t≤15min e.g.,20031201event.txt obtain (x0,y0), (t0,t1), size Event catalog in time order order, size estimate

  27. Contrast between personnel-made and machine-made catalogs (1)80% personnel-detected surges are recognized by machine (2)70% all dark events by personnel are also found by machine (3)355% new events added by machine (4)The events missed are due to…

  28. Comparisons of starting and ending times for 28 surges from SurgeM+P (a) t_personnel - t_machine (b) t_machine - t_personnel Starting times Ending times From this figure, we can see that the starting times decided by machine are usually earlier than by eye; and most of the ending times decided by machine are later than by eye.

  29. Some negative factors in the detection • Sunspots umbra region • Limb area • Data deleted for poor seeing • Resolution limitation • Some unexpected situation (airplane tracers, tree branches, etc) However, this program is designed to be able to overcome most of the shortages (except the resolution limitation) by carrying corresponding measures

  30. On detection of bright Ha features • Similar to FMT, SMART is also a multi-channel telescope, but it can supply high-resolution flare images. We wish with some efforts, the present procedure can be applied to SMART data for flares • There have been several methods for flare detections: (1) Automatic image segmentation and features detection in solar full-disk images, ESA SP-463,Veronig A. et al. (I_flare>2I_quiet) (2)Automatic solar flare detection using Neutral Network techniques, Solar Physics,2002 Borda R.F. et al. (3)Automatic solar flare detection using MLP, RBF,and SVM, Solar Physics, 2003, Qu M. et al. (4)Automatic solar flare tracking using image-processing techniques, Solar Physics, 2004, Qu M. et al. But they all use only Ha center data, SMART shall have the advantage to supply the important simultaneous off-band data.

  31. Conclusions • From the contrast to personnel-made catalogs, the results by the automatic program are proved to be robust in detecting dynamic dark Ha features with fast rate and more precise starting and ending times. 80%surges, 70%all dark events in the personnel catalogs are re-recognized, and 355% new events are added. • With some modifications, the procedure may be applied for detection of flare ribbons observed by SMART

  32. Threeinteresting surges in the seven days A surge ejected vertically from one point A surge ejected transversely from several points

  33. A surge ejected abnormally from outside towards the sunspot

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