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Automated Detection of Coronal Loops Using a Wavelet Transform Modulus Maxima Method |
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| Authors: | R T James McAteer Pierre Kestener Alain Arneodo Andre Khalil |
| Institution: | 1. Trinity College Dublin, College Green, Dublin 2, Ireland 2. CEA, Centre de Saclay, DSM/IRFU/SEDI, 91191, Gif-sur-Yvette, France 3. Laboratoire de Physique, école Normale Supérieure de Lyon, 46 allée d’Italie, 69364, Lyon cédex 07, France 4. Dept. of Mathematics, University of Maine, Orono, ME, 04469, USA |
| Abstract: | We propose and test a wavelet transform modulus maxima method for the automated detection and extraction of coronal loops in extreme ultraviolet images of the solar corona. This method decomposes an image into a number of size scales and tracks enhanced power along each ridge corresponding to a coronal loop at each scale. We compare the results across scales and suggest the optimum set of parameters to maximize completeness, while minimizing detection of noise. For a test coronal image, we compare the global statistics (e.g. number of loops at each length) to previous automated coronal-loop detection algorithms. |
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