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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 week13-17 November 2006

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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

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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 ?

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Wavelet transform: detect sharp discontinuitiesWavelet spectrum: summarizes wavelet transform

We use the CWT with Mexican Hat waveletsMexican Hat wavelets (MH):

The Wavelets spectrumWavelets 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

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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.

amax = 8.01

Log(a)

½. L

og(μ

(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...versus...

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B2X Catalog: examples

1998/05/01 23:15:15Position: S14W15Size: 23 pixels Goes Class: M1.2Intensity: 8914 DN/S

1998/05/02 13:42:05Position: S17W04Size: 25 pixels Goes Class: X1.1Intensity: 7282 DN/S

1998/05/06 09:24:23Position: S14W70Size: 35 pixels Goes Class: B3.1Intensity: 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

……

Beg

in o

f M

ay 1

998

Example : May 1998

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Correction of the limb discontinuityThe limb creates large wavelet coefficients and hence dominates the scale measure Replace the original image by

)( ),( iR eit RgxdyxIi

) ) _( ( * )(

)( )( disconImedian

Rg

RIRI eit

i

ieitieit

R/R0

I I

R/R0

Inte

nsi

ty

Originalimage

Limb corrected

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B2X-flare automatic detection and catalog

Website :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”

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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.

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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

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Multispectral aspect: combine 17.1 and 19.5 nm EIT images

1. Do fuzzy clusteringfuzzy clustering on each wavelength separately, get membership for pixel j

2.2. Combine membershipCombine 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)

3. Take a decisiondecision: attribute pixel j to class k for which it has the greatest membership.

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Example:1 feb 1998

17.1nm 19.5 nm

Fuzzy clustering

Aggregation,fusion

DecisionFused Segment.

Mono-spectralsegment.

AR

QS

CH

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17.1nm

19.5nm

28.4 nm

Other multi-channel approach: Segmentation of images using multi-dimensional fuzzy clustering

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Evolution of area of different regionsfrom February 1997 till May 2005 using segmentation on 17.1 and 19.5nm

Barra et alAdv Sp Res,submitted

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Find periodicities in time evolution of area from Active Regions

Periodicity in days

Peri

odic

ity in d

ays

25.9 days

2 years

Sum over the 3000 days, for each periodicity

2/1/1997 4/30/2005

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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.