Towards Automatic Tracking of Coronal Mass Ejections

Mathematics – Logic

Scientific paper

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Scientific paper

Tracing identifiable features of dynamical phenomena such as coronal mass ejections (CMEs), as they propagate through the corona, is a real challenge. In particular, different features from a single event usually display different velocities. Moreover, the lack of sharpness of the structures involved on top of the subjective nature of the measuring process makes it difficult to track the event unambiguously. To overcome the lack of sharpness, we developed a multiresolution image processing technique applicable to any 2D data set to enhance both boundaries and internal details of originally faint and diffuse structures. The method implemented employs a multi-level decomposition scheme (splitting algorithm of a wavelet packet on non-orthogonal wavelets) via the `a trous' wavelet transform, local noise reduction and interactive weighted recomposition. This approach represents a major advance towards unambiguous image interpretation and provides a means for the quantification of stationary and dynamic coronal structures required for conducting morphological studies. Moreover, it proved to be a necessary step in the development of a non-subjective technique for automatic tracking of CMEs. Examples based on LASCO-C1, -C2, -C3, and EIT data sets are shown. Different reconstruction strategies are also discussed.
This work is supported by NASA living with a Star and NSF/SHINE (ATM 0204588) Programs.

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