Solar Physics

, Volume 228, Issue 1–2, pp 97–117 | Cite as

Advanced Automated Solar Filament Detection And Characterization Code: Description, Performance, And Results

  • Pietro N. BernasconiEmail author
  • David M. Rust
  • Daniel Hakim


We present a code for automated detection, classification, and tracking of solar filaments in full-disk Hα images that can contribute to Living With a Star science investigations and space weather forecasting. The program can reliably identify filaments; determine their chirality and other relevant parameters like filament area, length, and average orientation with respect to the equator. It is also capable of tracking the day-by-day evolution of filaments while they travel across the visible disk. The code was tested by analyzing daily Hα images taken at the Big Bear Solar Observatory from mid-2000 until beginning of 2005. It identified and established the chirality of thousands of filaments without human intervention. We compared the results with a list of filament proprieties manually compiled by Pevtsov, Balasubramaniam and Rogers (2003) over the same period of time. The computer list matches Pevtsov's list with a 72% accuracy. The code results confirm the hemispheric chirality rule stating that dextral filaments predominate in the north and sinistral ones predominate in the south. The main difference between the two lists is that the code finds significantly more filaments without an identifiable chirality. This may be due to a tendency of human operators to be biased, thereby assigning a chirality in less clear cases, while the code is totally unbiased. We also have found evidence that filaments obeying the chirality rule tend to be larger and last longer than the ones that do not follow the hemispherical rule. Filaments adhering to the hemispheric rule also tend to be more tilted toward the equator between latitudes 10 and 30, than the ones that do not.


Space Weather Average Orientation Solar Observatory Code Result Visible Disk 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Pietro N. Bernasconi
    • 1
    Email author
  • David M. Rust
    • 1
  • Daniel Hakim
    • 1
  1. 1.Applied Physics LaboratoryJohns Hopkins UniversityLaurelU.S.A.

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