Difference: PhaseASmallEventDetection (r6 vs. r5)

eSDO 1121: Small Event Detection

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Deliverable eSDO-1121: Small Event Detection
V. Graffagnino, A. Fludra
07 September 2005


Small-scale transient brightenings are ubiquitous in the EUV images of the solar corona and transition region. These small events observed by SDO will be identified in AIA images over multiple wavelengths.

A number of algorithms will be explored and compared in an attempt to better identify actual small events, rather than including stochastic fluctuations/noise, whilst at the same time trying not to ‘lose’ events within the extraction/analysis process. A hybrid of the various algorithms may be required as each of the algorithms have their own particular strong and weak points.


  • Multi-wavelength AIA images – Both Low resolution full disk images and higher resolution images will be available as input


  • Processed images indicating event locations in each wavelength;
  • Statistical data extracted from the images including:
    • total number of events.
    • Size distributions
    • Duration distributions,
    • Peak intensity distributions
    • wavelength in which event has been detected
  • Catalogue access possibly via webservice/ASTROGRID
  • FITS file produced with table extensions.

Test Data

  • TRACE and SOHO EIT images in multiple wavelengths

Tool Interface

  • commandline: AIA image as input;
  • Distributed through SolarSoft and as AstroGrid CEA application
  • note: tool will run continuously in UK on new AIA data to generate catalogue

Science Use Case

The user would like to obtain full statistical information on the properties of small transient brightenings observed in the AIA images at the various wavelengths. The goal of the algorithm is to identify all small events regardless of the type (i.e. whether it is a blinker, micro-flare, bright point etc.). Identification of the type of event is left to the user.

The actual mechanics of use will be similar regardless of whether the tool is applied individually to a series of images by the user or whether the process is automated via a pipeline whose generated results are catalogued and then accessed by the user via a web service/AstroGrid. The user identifies a series of images from one or more of the AIA channels taken during the specified time period.

  1. The user inputs the flatfield AIA images to one or more of the automated small event detection algorithms.
  2. The algorithm runs and returns a FITS file to the user.
  3. The user can view an image within the FITS file displaying the location of the detected small events.
  4. 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.

Technical Use Case

Detection method of Berghmans et al. (1998)

  1. The automated loop recognition algorithm receives a series of AIA flatfield 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 with intensity peaks significantly above this background value (i.e. greater than a pre-defined number of standard deviations above the background value) are then defined as small events.
  4. The spatial and temporal extent of the event is then defined. defined, by examining surrounding pixels which experience increased intensity greater than another threshold level above the background value, a level which differs from the peak threshold value by one standard deviation.
  5. These pixels are then flagged so as to be excluded from subsequent analysis. This prevents the same brightenings as being counted again in a neighbouring pixels light curve.
  6. Relevant data are extracted for each event and tabulated.
  7. A FITS file is produced and returned.

Detection 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 loop recognition algorithm receives a series of AIA flatfield 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. This is defined as the temporal coincidence of peak flux 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. These difference values are then ordered.
  5. The macropixel with the largest flux variability is then chosen and neighbouring pixels examined for variability and coincidence of peak time within the tolerance limit. This continues until no further neighbouring pixel is found which corresponds to the appropriate tolerance limits.
  6. 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.
  7. The remaining pixels are resorted in order of flux variability and the process is repeated. In this way a number of events are defined.
  8. The relevent statistical information is derived.
  9. An output FITS file is produced and returned.

Quicklook Products

  • FITs file containing low resolution full disk image of Sun indicating positions of areas of interest .
  • Table entry in CME dimming region and small event catalogue

Support Information

  1. D.Berghmans, F.Clette, and D.Moses. Astron.Astrophys. 336,1039-1055 (1998)
  2. M.J.Aschwanden, R.W.Nightingale, T.D.Tarbell, and C.J.Wolfson. Ap.J. 535,1027-1046 (2000)

-- ElizabethAuden - 08 Sep 2005

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