Multiscale Detection and Characterisation of CMEs

Multiscale Detection and Characterisation of CMEs

J. P. Byrne1, P. T. Gallagher1, C. A. Young2 and

R. T. J. McAteer3

1 Astrophysics Research Group, School of Physics, Trinity College Dublin, Dublin 2, Ireland.2 ADNET Systems Inc., NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.

3 Catholic University of America, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA.

STEREO/Cor1 24-Jan-07

Overview

• CME Models

• Image Processing: Multiscale methods

• CME Morphology & Kinematics

• Application to LASCO and STEREO/SECCHI LASCO/C3 27-Feb-00

CME Models

• Magnetic Flux-Rope:

• Magnetic Break-out:

-Forbes & Priest, 1990-Chen & Krall, 2003

-Antiochos et al. 1999-Lynch et al. 2004

Image Pre-Processing

• Normalisation - exposure time- CCD bias- data dropouts

• Background subtraction

• Median filtering (de-noising)

LASCO/C2 18-Apr-00

Finding the CME Front

Edge Detection:

Our Algorithm

1) Multiscale Decomposition

Vector-Arrow Field

3) Spatio-Temporal Filter

4) Non-Maxima Suppression

5) CME Front Characterisation

Kinematics & Morphology


2) Gradient Space Information

Our Algorithm


CME Vector Flow Field






Wavelets:

- Scaling / dilation factor (a)- Shifting / translation factor (b)- Suppress noise

€

ψa,b (x) =1

bψ

x − b

a

⎛

⎝ ⎜

⎞

⎠ ⎟


LASCO/C2 24-Jan-07

Low pass: Approximation

High pass: Detail

Input: f


€

0

€

0

€

φ

€

∂φ

Vertical Direction:

Horizontal Direction:Scale 1

Scale 1 Scale 3 Scale 5

Scale 3 Scale 5


€

AN ∗∂yφ

€

AN ∗∂xφ


€

DN +1 = AN ∗∂xφ( ), AN ∗∂yφ( )[ ]

Consider the Gradient of an image:(which points in the direction of most rapid change)

We have the Detail at a scale (N+1) resulting from the directional Derivative-of-Gaussian convolved with the Approximation at scale (N):

The gradient specifies:

€

θ =tan−1 ∂f

∂y

∂f

∂x

⎛

⎝ ⎜

⎞

⎠ ⎟2) Direction:

1) Magnitude:

€

∇f =∂f

∂x

⎛

⎝ ⎜

⎞

⎠ ⎟2

+∂f

∂y

⎛

⎝ ⎜

⎞

⎠ ⎟

2

So too can the Magnitude and Direction be taken from the multiscale decomposition as illustrated…

Original: Magnitude:Angle:

€

∇f =∂f

∂x

⎛

⎝ ⎜

⎞

⎠ ⎟2

+∂f

∂y

⎛

⎝ ⎜

⎞

⎠ ⎟

2

€

θ =tan−1 ∂f

∂y

∂f

∂x

⎛

⎝ ⎜

⎞

⎠ ⎟

(McAteer et al. 2007)


€

∇fVectors with magnitude:and inclination angle:

€

θ

QuickTime™ and aH.264 decompressor

are needed to see this picture.


Our Algorithm








2) Spatio-Temporal Analysis

Degrees of Freedom: Scale, Magnitude & Angle … in Space & Time



Our Algorithm









1) Nearest-neighbour info.

2) Criteria of angle and magnitude from gradients.

3) Pixels chained along edges.

(Image by C.A.Young)

Our Algorithm








• Ellipse fit• Height, Width,

Curvature, Orientation


STEREO/Cor1 24-Jan-07

€

ρ2 =a2b2

a2 + b2

2

⎛

⎝ ⎜

⎞

⎠ ⎟−

a2 − b2

2

⎛

⎝ ⎜

⎞

⎠ ⎟cos(2 ′ θ − 2α )

€

ρ2 cos2 θ

a2+

ρ 2 sin2 θ

b2=1

(H.E. Schrank, 1961)

SOHO/LASCO CMEs

C2 & C3 18-Jan-00

C2 & C3 19-Apr-00

SOHO/LASCO CMEs

C2 & C3 23-Apr-01

SOHO/LASCO CMEs



STEREO/Cor1 CMEs

SECCHI-A 9-Feb-07

STEREO/Cor1 CMEs

SECCHI-A 24-Jan-07

STEREO/Cor1 CMEs

SECCHI-B & SECCHI-A 24-Jan-07

STEREO/Cor1 CMEs

SECCHI-A 24-Jan-07



LASCO/C2 24-Jan-07

CME Kinematics

Next Steps…

• More data; distribution of CME kinematics.

• Multiple view points (STEREO); triangulation / projection effects.

• Automated front detection; space weather forecasting.

STEREO illustration

Thank You

NRL:

Angelos Vourlidas, Simon Plunkett.

This work is supported by grants from Science Foundation Ireland & NASA’s Living with a Star Program.

[email protected]

Acknowledgments

C2 & C3 23-Apr-01

SOHO/LASCO CMEs

2) Spatio-Temporal Analysis


Vector Flow Field

€

∇fVectors with magnitude:and inclination angle:

€

θ

Normalizing Radial Graded Filter

• Radially the coronagraph image intensity drops off steeply.

• The intensity is normalized by subtracting the mean and dividing by the standard deviation.

€

′ I (r,φ) =I(r,φ) − I(r)<φ>

σ (r)<φ>

(Huw et al. 2006)

remove

Scale Chaining / Masks


=> Spatio-Temporal Image Processing & Thresholding

remove

Image preprocessing

Multiscale decomposition scale chaining (denoising masks)

Spatio-temporal filter (noisy masks)

Combine scale chain & spatiotemp => filter masks

Non-maxima suppression

Ellipse characterization

Gradient space vector field (angle & magnitude)

Method FlowChart

NRGF: radial filter

Movie Scripts

• http://www.maths.tcd.ie/~jaydog/Solar/canny_atrous/automation/20000118_arrows_combined_rebin.html



• http://www.maths.tcd.ie/~jaydog/Solar/CME_ellipse_movies/24jan07/C2_movie_ell.html

• http://www.maths.tcd.ie/~jaydog/Solar/CME_ellipse_movies/24jan07/C3_movie_ell.html

• www.maths.tcd.ie/~jaydog/Solar/STEREO/pb/24jan07/Graphs_plots.html

Remove

http://www.maths.tcd.ie/~jaydog/Solar/canny_atrous/automation/20000118_arrows_combined_rebin.html






http://www.maths.tcd.ie/~jaydog/Solar/CME_ellipse_movies/24jan07/C2_movie_ell.html

http://www.maths.tcd.ie/~jaydog/Solar/CME_ellipse_movies/24jan07/C3_movie_ell.html

http://www.maths.tcd.ie/~jaydog/Solar/STEREO/pb/24jan07/Graphs_plots.html

Documents

Multiscale Detection and Characterisation of CMEs