eSDO 1121: CME Dimming Region Recognition

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Deliverable eSDO-1121: CME Dimming Region Recognition
V. Graffagnino, A. Fludra
07 September 2005

Description

A coronal mass ejection results in a ‘dimming’ of the hot (EUV and X-ray) corona as a result of the opening of magnetic field lines, the expansion of coronal material and the resulting density reduction that occur during a CME. In attempting to identify the source region of CME events, a number of studies have characterised the phenomenon of coronal dimming (see for example the reviews of Hudson and Webb 1997, Harrison and Lyons 2000). These studies have been based on two types of difference images in which dimmed regions appear as dark features with a reduced intensity. The two types are:

  1. Running difference images, which are obtained by subtracting the preceding image from the current one. These emphasise changes in the brightness, localisation and structure of sources that have occurred during the interval between successive images. However, artefacts such as spurious dimmings can occur if the intensity of a bright feature decreases.
  2. Fixed difference images, which are produced by subtracting a single pre-event image from all subsequent images. Any changes that occur during an event are particularly clear. However unless solar rotation is taken into account potentially spurious dimmings and brightenings can be produced since the pre-event reference image may have been taken hours before subsequent frames.

Previous Coronal Dimming investigations have made use of wide-band soft X-ray imaging, narrowband EUV filters and EUV spectra. AIA narrow-band filters are therefore expected to allow the detection of such events. The identification of a CME signature such as Coronal

Dimming is critical to fully investigate the underlying physics of the CME onset. The goal of this algorithm is therefore to identify regions of coronal dimming in order to carry out follow up investigations of subsequent CME events.

Inputs

  • Multi-wavelength AIA images - The main algorithm will primarily employ Full Disk Low Resolution images. It is suggested however that the additional use of Half Disk High Resolution images be investigated during the development of the algorithm in order to better costrain the algorithm and see whether their incorporation into the algorithm is warranted.

Outputs

  • Processed images indicating CME Dimming Region locations in each of the filter wavelengths;
  • The output will be entered into the CME Dimming Region Catalogue where statistical data extracted from the images will be presented. This statistical data includes:
    • Size of the Dimming Region
    • Duration of the dimming,
    • Intensity variations during the Dimming
    • Wavelength in which event has been detected
  • FITS file produced with table extensions.
  • Catalogue access possibly via webservice/ASTROGRID

Test Data

  • TRACE and SOHO EIT images in multiple wavelengths

Tool Interface

  • commandline: The CME Dimming Region Recognition C code may be wrapped as an IDL application and distributed through the SolarSoft gateway at MSSL. Users will then be able to access the CME Dimming Region Recognition detection tool through SolarSoft installations at their institutions as a standalone tool, via the command line or for more intricate studies via the use of a GUI; If processing space is available on a web service enabled machine within the UK, the tool's C code can also be wrapped with the AstroGrid CEA software and made accessible as a web service or the catalogue produced in the pipeline could be accessed through a web service. However the tool will primarily be used, via the command line, to run continuously on new AIA data produced in the pipeline as input, in order to generate the CME Dimming Region Catalogue.

Science Use Case

The user would like to obtain physical and statistical information on the properties of possible CME dimming regions observed in the AIA images at a number of wavelengths. The goal of the algorithm is to identify these dimming regions, both on disk and above the limb. Follow up investigations of these regions resulting in the actual identification of a CME event is left to the user.

The actual mechanics of use will be similar regardless of whether the process is automated via a pipeline whose generated results are catalogued and then accessed by the user via a web service/AstroGrid etc., or whether the tool is applied individually to a series of images by the user using different constraints to define the level of dimming than those selected for the pipeline.

In the standalone case:

  1. The user identifies a series of images from one or more of the AIA channels taken during the specified time period.
  2. The user inputs the flatfield AIA images to one or more of the automated CME Dimming Region recognition algorithms.
  3. The user specifies the constraints used to identify the level of dimming
  4. The algorithm runs and returns a FITS file to the user.
  5. The user can view an image within the FITS file displaying the location of the CME Dimming region.
  6. A fits table will also be available to allow standard statistical analysis to be carried out and also allows the user to carry out further analysis on each event(s) as desired.

In the pipeline scenario:

  1. The user would like to obtain physical and statistical information such as location, size, duration and intensity variations of CME dimming regions held within the CME Dimming Region Catalogue, which is being constantly updated by new AIA data from the pipeline.
  2. The flatfielded pipeline AIA images are inputted to one or more of the automated CME Dimming Region recognition algorithms.
  3. The algorithm runs and returns a FITS file which is entered into the CME Dimming Region Catalgue.
  4. The user can search the catalogue and view an image within the FITS file displaying the location of the CME Dimming region.
  5. A fits table is also produced giving location, size, duration and intensity variations of CME dimming region. This can be used to allow standard statistical analysis to be carried out.
  6. The supplied metadata can then be used to further analyse the relevant AIA images and/or obtain complementary data (e.g. from STEREO) on each event(s) as desired.

Technical Use Case

The CME Dimming Region Recognition code should be available both as standalone and also wrapped as an IDL application which can then be distributed through the SolarSoft gateway at MSSL. In essence the technical aspect of the procedure has much in common with the Small Event Detection algorithms being investigated, and so will use the methods of Berghmans et al. (1998), and Aschwanden et al. (2000) with the main difference being that drops in intensity over large areas, lasting for tens of minutes up to several hours, are being looked for rather than small, short-lived increases in localised areas. Much of the small event detection code will therefore be re-used. Also the code would need to be able to group together a much larger number of pixels that undergo simultaneous dimming to find the extent of the dimming region. The other main difference to the small event detection algorithm is that the processing can additionally use fixed difference AIA images in order to clearly identify regions of dimming. For on-disk areas, corrections to take into account solar rotation will also have to be applied to these difference images in order to reduce the number of spurious dimming regions that might be identified.

Modified Method of Berghmans et al. (1998)

  1. The automated CME Dimming Region recognition algorithm receives a series of AIA flatfield images and fixed difference AIA images as input.
  2. An average light curve for each pixel over a set time period is derived to define a background reference emission.
  3. Pixels, where intensity drops near-simultaneously and significantly below this background value, are then defined as CME Dimming events.
  4. The spatial and temporal extent of the event is then defined.
  5. Relevant data are extracted for each event and tabulated.
  6. A FITS file is produced and returned.

Modified Method of Aschwanden et al. (2000)

In this method, a spatio-temporal pattern recognition code is used which extracts events with significant variability. This is achieved as follows:

  1. The automated CME Dimming Region recognition algorithm receives a series of AIA flatfield images and fixed difference AIA images as input.
  2. Each full image is rebinned into a number of macropixels.
  3. An event is then spatially defined to include a number of neighbouring pixels that undergo coherent time variability, within a certain tolerance limit.
  4. For each macropixel the time series is examined and the maximum, minimum fluxes and corresponding times are extracted and a flux variability defined as the difference is derived.
  5. These difference values are then ordered.
  6. The macropixel with the largest flux decrease is then chosen and neighbouring pixels examined for variability and temporal coincidence of the minimum of the lightcurve within the tolerance limit. This continues until no further neighbouring pixel is found which corresponds to the appropriate tolerance limits.
  7. This collection of pixels is then defined as an event and these pixels are marked so as not to be included in subsequent events searches.
  8. The remaining pixels are resorted in order of flux variability and the process is repeated. In this way a number of dimming events are defined, although it is expected that simultaneous, spatially independent dimming events will occur rarely.
  9. The relevent statistical information is derived.
  10. An output FITS file is produced and returned.

Quicklook Products

  • A FITS file containing low resolution full disk image of the Sun indicating positions of dimming regions together with a table extension with the relevant statistical data will be available in the CME Dimming Region Recognition catalogue.

Support Information

  1. K.P. Dere, G.E. Brueckner, R.A. Howard et al. Solar Physics. 175, 601-612 (1997).
  2. I.M. Chertok and V.V. Grechnev, Astronomy Reports. 47, 139-150 (2003).
  3. I.M. Chertok and V.V. Grechnev, Astronomy Reports, 47, 934-945 (2003).
  4. D. Berghmans, F. Clette, and D. Moses. Astron.Astrophys. 336,1039-1055 (1998)
  5. M.J. Aschwanden, R.W. Nightingale, T.D. Tarbell, and C.J. Wolfson, Ap.J. 535,1027-1046 (2000)
  6. Hudson, H.S., Webb, D.F., in: Coronal Mass Ejections, Geophys. Monograph Ser., AGU. 1, (1997).
  7. Harrison, R.A., Lyons, M., A&A, 358, 1097, (2000)

-- ElizabethAuden - 08 Sep 2005

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