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Coronal seismology, AIA/HMI and image processing (-: Best wishes :-) JF Hochedez, E Robbrecht, O Podladchikova, A Zhukov, D Berghmans ROB Solar.

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Presentation on theme: "Coronal seismology, AIA/HMI and image processing (-: Best wishes :-) JF Hochedez, E Robbrecht, O Podladchikova, A Zhukov, D Berghmans ROB Solar."— Presentation transcript:

1 Coronal seismology, AIA/HMI and image processing (-: Best wishes :-) JF Hochedez, E Robbrecht, O Podladchikova, A Zhukov, D Berghmans ROB Solar Influences Data analysis Center Royal Observatory of Belgium

2 AIA Coronal Seismology Image Processing Mandate of this presentation

3 EUV imaging observations and seismology (1) in [simple] flux tube magnetic structures Fast magneto-sonic modes Slow Magneto-sonic (sausage) mode KinkSausage Standing TRACE 1MK 1999 (Aschwanden et al., Nakariakov et al.) AIA 2009? SUMER 6MK 2002 (Kliem et al., Wang et al.) Propagating TRACE 20MK 2005 (Verwichte et al) AIA 2009? EIT 1MK 1998 Deforest & Gurman Berghmans & Clette 99, TRACE... Optical Flow Motion & brightness change tracking Loop recognition and Cactus-like approach x-t diagrams, Hough transform, clustering

4 EUV imaging observations and seismology (2) in [other] coronal structures Global EIT waves EIT Thompson et al 1998 Prominence oscillations not discussed in this talk but not forgotten Oscillations and waves during eruptions (CME or flares) The future? But challenging! Sympathetic flares EIT wave detector Flare detector and Podladchikova et al (submitted)

5 Presentation sections 1.When Optical Flow will detect fast modes in flux tubes 2.Loop recognition and Hough transform applied to slow waves 3.What EIT waves can tell us about the corona 4.[Prospective] sympathetic flares. How do they communicate? 5.Conclusions

6 Optical Flow & its application to fast modes

7 Remaining problems with kink oscillations Damping –Test competing explanations phase mixing resonant absorption (Goosens et al 2002) leakage at footpoints, others… –Too many parameters stratification (estimated by Andries et al 2005) Curvature variable cross-section  More statistics needed Exciter(s) –Their nature? From below? From side? Why so few ? –Damping or lack of exciters?

8 Hopes from AIA-HMI (1/2) 8 bandpasses –Longitudinal density profile (DEM tools) –Heating profile Spatial resolution –Radial density profiles: concentric shells, threads? 0.6”probably still too low –Overtones (Verwichte et al 2004) –3D geometry with Secchi Loop length vertical vs swaying (Wang et Solanki 2004), etc. Full Sun FOV –2 pressure scale heights long loops with good SNR –With temporal coverage: statistics

9 Hopes from AIA-HMI (2/2) 2s Cadence –time aliasing repressed –SNR  Time rebinning –exposure time ~0.1s Less kinetic blurring Stroboscopy –Observe fast sausage waves, fast sausage oscillations, fast propagating kink waves! Effective area (44x –See smaller disturbances. Presence of HMI –Independent estimate of B (cf. too many parameters) Compatible with seismology? (NLFF and dynamics) AIA trade-off TBD

10 VELOCIRAPTOR VELOCIty & bRightness vAriations maPs construcTOR Quantify motion together with intrinsic brightness variation in EIT image sequences Gissot & Hochedez, 2006

11 Inputs & outputs Velocity field Image I n (x,y) Image I n+1 (x,y) Brightness Variation field 1.Similarity field between I n (x,y) (warped) and I n+1 (x,y) 2.Local “texture” 3.Residuals e.g. EIT “CME Watch” Hochedez & Gissot

12 Differential rotation recovered from a couple of EIT images (No BV estimation)

13 BV map of the May 12, 1997 event

14 Velocity map of the May 12, 1997 event

15 (No BV estimation)

16 14 July :50:16

17 Differenced image

18 Velocity field

19 Presence of texture in at least one direction (zoom)

20 Average displacement ~0.2 pixel → LCT not appropriate (a posteriori justification) Velocity field produced by Velociraptor

21 Question: What are the anticipated artifacts for AIA?

22 OF & fast magneto-sonic waves: Conclusions and outlook Velociraptor can measure sausage and kink waves –Precisely, all along the loops, systematically, Outliers? –Challenging development –Being fully calibrated –2 main problems understood and being corrected: Strong BV  fictive motion Some spurious sliding remains along loops Post-processing of the fields needed in order to identify waves autonomously (1D wavelets?) AIA + OF  great prospect –Sausage modes by EUV imaging? –Flows from steady reconnections? –Mode coupling?

23 Slow waves

24 Good overall understanding but … Wave or plasma motion? (no Doppler measurements) Sound speed if pattern seen in several BPs cf. Robbrecht et al EIT vs TRACE Klimchuk et al 2004: –Their study validates classical thermal conduction damping –But “TRACE loops are inconsistent with static equilibrium and steady flow” –“Observed damping times of slow mode oscillations might be a lower limit to effective damping times, which can only be corrected if the cooling time is known from multi-filter data.” Seismology is complementary to DEM

25 Useful image processing for slow waves (1) Loop extraction (ridge detection)

26 Useful image processing for slow waves (2) Analysis of X-T diagrams –Hough Transform –Clustering –Cf “CACTUS” applied to [faint] CME detection in LASCO C2 & C3

27 15h1815h5417h06 11 November 2003 Computer Aided CME Tracking -CACTus

28 t r t0t0 ΔtΔt

29 EIT waves

30 EIT waves for coronal seismology EIT waves: bright fronts propagating from eruption sites observed in EUV (SOHO/EIT, TRACE, CORONAS-F/SPIRIT, 195 Å, 171 Å, 284 Å bandpasses). Sometimes EIT waves propagate nearly isotropically and often – globally. EIT wave speeds are usually about 150–400 km/s, typically around 250 km/s. Association with chromospheric Moreton waves, waves in He I and waves in SXR?

31 If EIT waves are fast magnetosonic waves… Fast magnetosonic wave speed around 250 km/s means  ~ 1 or  > 1 in the “quiet Sun” corona Force-free approximation is not valid! ** Wang (2000) Wu et al. (2001) Courtesy A Zhukov 2006

32 a quantitative investigation DIMMING & EIT wave extraction from EUV image EIT wave radial and polar analysis Brightness distribution (histogram) analysis study of higher moments Ring Analysis radial velocities in the EIT wave Angular-Ring Analysis potential angular features Podladchikova & Berghmans, 2005

33 Skewness & Kurtosis of PDF of difference image versus time Simultaneous peaks + dimming area criteria → EIT Waves! Courtesy of Podladchikova & Berghmans

34 12 May 1997 Distances vs TimeIntegrated signals vs Time Courtesy of Podladchikova & Berghmans Both quadratic Width m3-m2 m max

35 Results 1.Anisotropy even without obstacles. Correlation with associated dimming; 2.Dimming contiguous to wave front in all directions 3.Width of the front grows ~quadratically in time; 4.Integrated intensity of wave front grows during > 1/2 hr The front intensity of linear magnetosonic waves would decrease 5.Integrated intensity of front balances integrated intensity of the dimmings (in early life of wave) EIT wave = MHD wave?

36 Sympathetic flaring

37 Consecutive occurrence of flares in different AR

38 3225 flares registered with coordinates since 01/01/2004. Statistically complete series. Result does not depend on time interval Velocity [km/s] Perturbation velocity from flare to flare “to set the fire” V char ~ 110 km/s  t < 5h.

39 Conclusion significant number of events where one flare “sets fire” triggering another distant flare in a separate active region. Propagation velocities for such perturbations around 110 km/s.

40 B2X flare detector Just before the flare begins At flare peak log(scale) ½ log(μ(scale)) Method: Wavelet spectrum (scale measure) analysis Hochedez et al ’02 Solspa2 Proc., Delouille et al SoPh ’05 Result: Small flares automatic detection Relevance: Sympathetic flaring studies

41 Beauty spotter Method: Extraction in scale space by Lipschitz coefficient Hochedez et al 2002, Soho11 WS Proc., Hochedez et al 2003 Soho13 WS Result: BPs, brightenings and Cosmic Ray Hits extracted Relevance: Oscillations in point-like structures

42 Conclusions The easy things about waves have been found. Intelligent techniques can invigorate future research –Prospect for eruption precursors? Image processing = binding agent between theory and observation –Like an additional "telescope" for small scale physics improve resolution separate different processes (mutually and from noise) extract waves or reconnection events part intensity from velocity variations –Like a new "microscope" for large scale physics Describe of important events "in situ sensor“, identifying the nature of events Uncover unexpected regularities For all these reasons, all detected waves should go in the SDO catalogs


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