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Detection and Analysis of Magnetic Fragments within Solar Active Regions

Detection and Analysis of Magnetic Fragments within Solar Active Regions. Fraser Watson, University of Glasgow Lyndsay Fletcher, University of Glasgow Stephen Marshall, University of Strathclyde. The Goal.

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Detection and Analysis of Magnetic Fragments within Solar Active Regions

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  1. Detection and Analysis of Magnetic Fragments within Solar Active Regions Fraser Watson, University of Glasgow Lyndsay Fletcher, University of Glasgow Stephen Marshall, University of Strathclyde SIPwork V, Wed September 15th 2010, Les Diablerets, Switzerland

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

  3. 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)

  4. 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.

  5. 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.

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

  7. An example First of all, the algorithm searches for the largest pixel value Increasing magnetic field strength

  8. An example 1 First of all, the algorithm searches for the largest pixel value This pixel is assigned the label of ‘Region 1’ Increasing magnetic field strength

  9. An example 1 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 Increasing magnetic field strength

  10. An example 2 1 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’. Increasing magnetic field strength

  11. An example 3 2 1 This continues until a pre-defined threshold is reached. This depends on the instrument used. Higher thresholds mean faster processing. Increasing magnetic field strength

  12. An example 3 2 1 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. Increasing magnetic field strength

  13. The Method • 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.

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

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

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

  17. 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.

  18. Future development • Most of this stems from the problems with oversegmentation and merging fragments.

  19. 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

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