1. 1 Automatic flare detection and tracking of active regions in EUV images. Véronique Delouille Joint work with Jean-François Hochedez (ROB), Judith de Patoul (ROB), and Vincent Barra (LIMOS) www.sidc.be European Space Weather week 13-17 November 2006

2. 2 EUV images analysis for Space Weather  Previous talk: detection of dimmings and EIT-waves using NEMO (Elena Podladchikova & David Berghmans, 2005)  Current talk: Detection of brightness enhancement in EUV images, i.e. flares Automatic segmentation of EUV images in order to, e.g., localize Coronal Holes and Active Regions

3. 3 Detection of brightness enhancement in EUV images  Aim : Decide if a flare is happening (or not) on a given EUV image. If yes, give all characteristics such as localization, size, intensity, time duration,… Build catalog of EUV flares  Tool : Mexican Hat continuous wavelet transform, summarized into the scale measure, also called ‘wavelet spectrum’ Flaring or non flaring ?

4. 4 Wavelet transform: detect sharp discontinuities Wavelet spectrum: summarizes wavelet transform Mexican Hat wavelets We use the CWT with Mexican Hat wavelets (MH): Wavelets spectrum The Wavelets spectrum is obtained by integrating the wavelet coefficients over real space:  The shape of this spectrum will be analyzed to select images containing flares.  To work (and detect flares) at the limb, we have to correct for its discontinuity. The Mexican Hat wavelet Hochedez et al 2002 Solspa2 Proc. Delouille et al Solar Physics, 2005 a

5. 5 Flares dominate medium scales in images; the scale measure presents a characteristic scale. No flare situation: μ(a) is linear in log-log scale with a positive slope. a max = 8.01 Log(a) ½. Log(μ(a)) 1998/05/01 02:34:17 1998/05/01 23:15:15 CWT at the characteristic scale B2X : detection of flares in EIT images … versus...

6. 6 B2X Catalog: examples 1998/05/01 23:15:15 Position: S14W15 Size: 23 pixels Goes Class: M1.2 Intensity: 8914 DN/S 1998/05/02 13:42:05 Position: S17W04 Size: 25 pixels Goes Class: X1.1 Intensity: 7282 DN/S 1998/05/06 09:24:23 Position: S14W70 Size: 35 pixels Goes Class: B3.1 Intensity: 1960 DN/S ½. Log(μ(a)) vs log(a) … 1998/05/27 11:19:53 FLARE Position: S15.85W65.11 Size=38.72 1998/05/27 11:37:37 FLARE Position: S17.17W65.11 Size= 8.32 1998/05/27 11:49:19 FLARE Position: S16.85W66.11 Size= 8.13 … Log(a) 0 0.5 1 1.5 2 2.5 3 3.5 Min energy Max energy … Begin of May 1998 Example : May 1998

7. 7 Correction of the limb discontinuity The limb creates large wavelet coefficients and hence dominates the scale measure  Replace the original image by R/R 0 I Intensity Original image Limb corrected

8. 8 B2X-flare automatic detection and catalog Website : http://sidc.be/B2X/ Poster of Judith de Patoul : Poster of Judith de Patoul on Wednesday : “ “An automatic flare detection for building EUV flare catalog”

9. 9 Multispectral segmentation of EUV images  Aim: separate Coronal Holes (CH), Quiet Sun (QS), and Active Regions (AR) : Localize CH (source of fast solar wind) Localize AR (source of flares) … But also … Analyze time series evolution of area, mean intensity, cumulated intensity of CH, QS, AR separately Bridge the gap between imager telescope and radiometers.

10. 10 Fuzzy clustering : principle and advantages  Non-fuzzy clustering: attribute to each pixel j a label to a class k Є {CH, QS, AR} E.g.: pixel j belong to class AR  Fuzzy clustering: attribute a membership value to a class k E.g.: pixel j belong 80% to AR, 20% to QS  Advantage of Fuzzy Clustering: uncertainty present in the images is better handle (noises, separation between types of regions not clear-cut) Inclusion of human expertise is possible

11. 11 Multispectral aspect: combine 17.1 and 19.5 nm EIT images fuzzy clustering 1. Do fuzzy clustering on each wavelength separately, get membership for pixel j 2. Combine membership 2. Combine membership for pixel j using a Fusion Operator:  If information between wavelength is consistent, operator retains the most pertinent information, i.e. it takes the minimum of memberships from 17.1 and 19.5 nm  If information do not agree, operator acts cautiously, and takes the maximum of both memberships (acts as ensemblist union) decision 3. Take a decision: attribute pixel j to class k for which it has the greatest membership.

12. 12 Example: 1 feb 1998 17.1nm19.5 nm Fuzzy clustering Aggregation, fusion Decision Fused Segment. Mono- spectral segment. AR QS CH

13. 13 17.1nm 19.5nm 28.4 nm Other multi-channel approach: Segmentation of images using multi-dimensional fuzzy clustering

14. 14 Evolution of area of different regions from February 1997 till May 2005 using segmentation on 17.1 and 19.5nm Barra et al Adv Sp Res, submitted

15. 15 Find periodicities in time evolution of area from Active Regions Periodicity in days 25.9 days 2 years Sum over the 3000 days, for each periodicity 2/1/19974/30/2005

16. 16 Conclusion  On-disc flare detection using B2X Study characteristics of EUV flares: statistics on their duration, position, size, etc,... Catalog and real-time detection  Segmentation of EUV images Automatic tracking of coronal holes and Active region Separation contribution to intensity from CH, QS, AR Analyses of periodicity in area, mean intensity, cumulated intensity.