Solar Physics

, 294:158 | Cite as

AWARE: An Algorithm for the Automated Characterization of EUV Waves in the Solar Atmosphere

  • Jack IrelandEmail author
  • Andrew R. Inglis
  • Albert Y. Shih
  • Steven Christe
  • Stuart Mumford
  • Laura A. Hayes
  • Barbara J. Thompson
  • V. Keith Hughitt


Extreme ultraviolet (EUV) waves are large-scale propagating disturbances observed in the solar corona, frequently associated with coronal mass ejections and flares. They appear as faint, extended structures propagating from a source region across the structured solar corona. Since their discovery, over 200 papers discussing their properties, causes, and physical nature have been published. However, despite this their fundamental properties and the physics of their interactions with other solar phenomena are still not understood. To further the understanding of EUV waves, we have constructed the Automated Wave Analysis and Reduction (AWARE) algorithm for the measurement of EUV waves. AWARE is implemented in two stages. In the first stage, we use a new type of running difference image, the running difference persistence image, which enables the efficient isolation of propagating, bright wavefronts as they travel across the corona. In the second stage, AWARE detects the presence of a wavefront, and measures the distance, velocity, and acceleration of that wavefront across the Sun. The fit of propagation models to the wave progress isolated in the first stage is achieved using the Random Sample Consensus (RANSAC) algorithm. AWARE is tested against simulations of EUV wave propagation, and is applied to measure EUV waves in observational data from the Atmospheric Imaging Assembly (AIA). We also comment on unavoidable systematic errors that bias the estimation of wavefront velocity and acceleration. In addition, the full AWARE software suite comes with a package that creates simulations of waves propagating across the disk from arbitrary starting points.


Corona, waves Propagation, coronal seismology 



We are grateful to the developers of SSWIDL (Freeland and Bentley, 2000), IPython (Pérez and Granger, 2007), SunPy (SunPy Community et al., 2015), matplotlib (Hunter, 2007), Scikit-Learn (Pedregosa et al., 2011), SymPy (Meurer et al., 2017), and the Scientific Python stack for providing data preparation, manipulation, analysis, and display packages. LH and JI acknowledge the support of the Solar Data Analysis Center (SDAC) through the SESDA grant 80GSFC17C0003.

Disclosure of Potential Conflicts of Interest

The authors declare that they have no conflicts of interest.


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© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  1. 1.NASA Goddard Spaceflight CenterGreenbeltUSA
  2. 2.Catholic University of AmericaWashington, DCUSA
  3. 3.University of SheffieldSheffieldUK
  4. 4.Trinity College DublinDublinIreland
  5. 5.Laboratory of Cancer Biology and Genetics, NCINIHBethesdaUSA

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