coronal seismology, aia/hmi and image processing (-: best wishes :-)
DESCRIPTION
Coronal seismology, AIA/HMI and image processing (-: Best wishes :-). JF Hochedez, E Robbrecht, O Podladchikova, A Zhukov, D Berghmans SIDC @ ROB Solar Influences Data analysis Center Royal Observatory of Belgium. Mandate of this presentation. AIA. Coronal Seismology. Image Processing. - PowerPoint PPT PresentationTRANSCRIPT
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Coronal seismology, AIA/HMI and image processing
(-: Best wishes :-)
JF Hochedez, E Robbrecht, O Podladchikova, A Zhukov, D Berghmans
SIDC @ ROBSolar Influences Data analysis Center
Royal Observatory of Belgium
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AIA
CoronalSeismology
ImageProcessing
Mandate of this presentation
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EUV imaging observations and seismology(1) in [simple] flux tube magnetic structures
Fast magneto-sonic modes Slow
Magneto-sonic (sausage) modeKink Sausage
StandingTRACE 1MK 1999
(Aschwanden et al., Nakariakov et al.)
AIA 2009?SUMER 6MK 2002
(Kliem et al.,Wang et al.)
PropagatingTRACE 20MK 2005
(Verwichte et al)AIA 2009?
EIT 1MK 1998
Deforest & Gurman
Berghmans & Clette 99, TRACE...
Optical Flow
Motion & brightness changetracking
Loop recognition andCactus-like approach•x-t diagrams,•Hough transform,•clustering
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EUV imaging observations and seismology(2) in [other] coronal structures
Global EIT wavesEIT
Thompson et al 1998
Prominence oscillationsnot 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)
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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
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Optical Flow
& its application to fast modes
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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?
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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
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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 TRACE@171, 61x @194)– See smaller disturbances.
• Presence of HMI– Independent estimate of B (cf. too many parameters)
• Compatible with seismology? (NLFF and dynamics)
AIA trade-off TBD
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VELOCIRAPTOR
VELOCIty & bRightness vAriations maPs construcTOR
Quantify motion together with intrinsic brightness variation
in EIT image sequences
Gissot & Hochedez, 2006
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Inputs & outputsVelocity
field
Image In(x,y)
Image In+1(x,y)
BrightnessVariation
field
1. Similarity fieldbetweenIn(x,y) (warped)and In+1(x,y)
2. Local “texture”3. Residuals
e.g. EIT “CME Watch”
Hochedez & Gissot
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Differential rotation recovered from a couple of EIT images
(No BV estimation)
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BV map of the May 12, 1997 event
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Velocity map of the May 12, 1997 event
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(No BV estimation)
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14 July 1998 12:50:16
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Differenced image
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Velocity field
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Presence of texture in at least one direction (zoom)
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Average displacement ~0.2 pixel
→ LCT not appropriate (a posteriori justification)
Velocity field produced by Velociraptor
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Question: What are the anticipated artifacts for AIA?
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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?
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Slow waves
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Good overall understandingbut …
• Wave or plasma motion? (no Doppler measurements)• Sound speed if pattern seen in several BPs• cf. Robbrecht et al. 2001 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
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Useful image processingfor slow waves (1)
• Loop extraction (ridge detection)
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Useful image processingfor slow waves (2)
• Analysis of X-T diagrams– Hough Transform– Clustering
– Cf “CACTUS” applied to [faint] CME detection • in LASCO C2 & C3
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15h18 15h54 17h06
11 November 2003
Computer Aided CME Tracking -CACTus
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tr
t0Δt
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EIT waves
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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?
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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
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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
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Skewness & Kurtosis of PDF of difference image versus time
Simultaneous peaks+ dimming area criteria
→ EIT Waves!
Courtesy of Podladchikova & Berghmans
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12 May 1997
Distances vs Time Integrated signals vs TimeCourtesy of Podladchikova & Berghmans
Both quadratic
Widthm3-m2
mmax
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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 hrThe 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?
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Sympathetic flaring
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Consecutive occurrence of flares in different AR
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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”
Vchar ~ 110 km/s
t < 5h.
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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.
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B2X flare detector
Just before the flare begins
At flare peak
log(scale)
½ log(μ
(sca
le))
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
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Beauty spotterMethod: Extraction in scale space by Lipschitz coefficientHochedez 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
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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