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We study the internal structure of coronal mass ejections (CMEs) using wavelet analysis. We derive wavelet spectra, spatially integrated over regions of interest within LASCO C2 white-light coronographic images. These spectra show an inflection point, which we use to characterize each spectrum. In a diagram of flux vs. spatial scale of the inflection point, we find that the analyzed structures fall into two, distinct groups: a lowflux, small-spatial-scale group (which we call the "homogeneous" type), and a high-flux, larger-spatial-scale group (the "collimated" type). Interestingly, if we study different regions of a given image, all of the structures fall into one of the two groups described above. From a qualitative comparison with the images, it is clear that the two groups identified by the wavelet analysis correspond to two types of morphologies, which are seen as either more-homogeneous or more-collimated structures.
We study the internal structure of coronal mass ejections (CMEs) using wavelet analysis. We derive wavelet spectra, spatially integrated over regions of interest within LASCO C2 white-light coronographic images. These spectra show an inflection point, which we use to characterize each spectrum. In a diagram of flux vs. spatial scale of the inflection point, we find that the analyzed structures fall into two, distinct groups: a lowflux, small-spatial-scale group (which we call the "homogeneous" type), and a high-flux, larger-spatial-scale group (the "collimated" type). Interestingly, if we study different regions of a given image, all of the structures fall into one of the two groups described above. From a qualitative comparison with the images, it is clear that the two groups identified by the wavelet analysis correspond to two types of morphologies, which are seen as either more-homogeneous or more-collimated structures.
Continuous wavelet analysis is gaining popularity in science and engineering for its ability to analyze data across both spatial and scale domains simultaneously. In this study, we introduce a wavelet-based method for halo identification and assess its feasibility in two-dimensional (2D) scenarios. We begin by generating four pseudo-2D data sets from the SIMBA dark matter simulation by compressing thin slices of three-dimensional (3D) data into 2D. Subsequently, we calculate the continuous wavelet transform (CWT) directly from the particle distributions, identify local maxima that represent actual halos, and segment the CWT to delineate halo boundaries. A comparison with the traditional friends-of-friends (FOF) method shows that while our CWT-identified halos contain slightly fewer particles, they have smoother boundaries and are more compact in dense regions. In contrast, the CWT method can link particles over greater distances to form halos in sparse regions due to its spatial segmentation scheme. The spatial distribution and halo power spectrum of both CWT and FOF halos demonstrate substantial consistency, validating the 2D applicability of CWT for halo detection. While our identification scheme has been tested solely in a limited 2D context and has shown some performance limitations, its linear time complexity of O ( N ) and consistency with the FOF method suggest its suitability for analyzing significantly larger data sets in the future and its potential to be extended to the 3D case.
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