Fraser Watson, University of Glasgow

Lyndsay Fletcher, University of Glasgow

Stephen Marshall, University of Strathclyde

Detection and Analysis of Magnetic Fragments within Solar Active

Regions

SIPwork V, Wed September 15th 2010, Les Diablerets, Switzerland

To create an efficient and robust method for detecting and tracking magnetic flux concentrations within active regions by examining magnetograms.

The Goal

To create an efficient and robust method for detecting and tracking magnetic flux concentrations within active regions by examining magnetograms.

Developed using MDI data (1024 by 1024 image, 96 minute cadence) but had to be adaptable and fast enough to handle data from the HMI instrument on SDO (4096 by 4096 image, order of minute cadence)

The Goal

Small fragments are detected by an algorithm which identifies the pixels at local maxima and ‘flood fills’ into surrounding pixels using a ‘downhill’ method.

The Method

Small fragments are detected by an algorithm which identifies the pixels at local maxima and ‘flood fills’ into surrounding pixels using a ‘downhill’ method.

This is similar to watershed based techniques and tends to oversegment flux within active regions. To fix this, very small elements are merged into larger elements that they are directly connected to.

The Method

Small fragments are detected by an algorithm which identifies the pixels at local maxima and ‘flood fills’ into surrounding pixels using a ‘downhill’ method.

This is similar to watershed based techniques and tends to oversegment flux within active regions. To fix this, very small elements are merged into larger elements that they are directly connected to.

The Method

We treat the image and magnetic field strength values as a 3D surface with peaks

and valleys.

An exampleIn

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First of all, the algorithm

searches for the largest pixel

value

An exampleIn

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First of all, the algorithm

searches for the largest pixel

value

This pixel is assigned the

label of ‘Region 1’

1

An exampleIn

creasin

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netic fi

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First of all, the algorithm

searches for the largest pixel

value

This pixel is assigned the

label of ‘Region 1’

The algorithm continues to

search for the largest unlabeled

pixel

1

An exampleIn

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Once the line has been reached, a

pixel is found that is not

connected to any pixel in region 1. This is the seed pixel of ‘Region

2’.

1 2

An exampleIn

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This continues until a pre-

defined threshold is

reached.

This depends on the instrument

used.

Higher thresholds mean

faster processing.

1 23

An exampleIn

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If only a static threshold was

used, regions 1 and 3 may be considered as

one fragment but detecting ‘downhill’

eliminates this.

However, very small separate peaks are also

classed as separate

fragments.

We can then merge very small

segments into larger nearby

ones.

1 23

So there is a balancing act between algorithm speed, splitting apart flux elements, and including as much of the active region flux as possible.

The code currently analyses a full disk MDI image in 5-10 seconds and returns all positive and negative flux elements that fit the criteria.

A catalogue is also created.

The Method

What can we learn?How complex are the regions studied?

What can we learn?How does the flux diffuse out from the centre of the region?

What can we learn?Are the flux locations affected by plasma flows?

What about SDO?

The code has already been used on HMI data successfully, although not on a large active region!This movie is from a small flux concentration in May 2010.

Most of this stems from the problems with oversegmentation and merging fragments.

Future development

What next? We will be improving the code, both in terms of detection method and

efficiency; firstly trying a multi-scale approach. This work will be done in collaboration with Prof. Stephen Marshall at the University of Strathclyde.

We are working with colleagues in the Max Planck Institute in Lindau, Germany to determine how strongly the photospheric flows are tied to the magnetic field and how they affect one another.

We also get information of the net movement of flux as well as emergence rates and will be comparing this with flare catalogues to see if the distribution of flux is related to the frequency or type of flares observed.

The technique is part of the ISSI Soldyneuro project and is used in collaboration with other members from all over Europe and the U.S.

SIPwork V, Wed September 15th 2010, Les Diablerets, Switzerland