Presentation #107.30 in the session “ISM/Galaxies/Clusters (Poster)”.
Some extended sources, among which we find the supernovae remnants or the galaxy clusters, present an outstanding diversity of morphologies that the current generation of spectro-imaging telescopes can detect with an unprecedented level of details. However, the data analysis tools currently in use in the high energy astrophysics community fail to take full advantage of these data : most of them only focus on the spectral information without using the many spatial specificities or the correlation between the spectral and spatial dimensions. For that reason, the physical parameters that are retrieved are often widely contaminated by other components. Here, we will explore a new blind source separation method exploiting fully both spatial and spectral information with X-ray data, and their correlations. We will introduce the mathematical concepts on which the algorithm relies, and present some current physical applications on SNRs and future studies that it could benefit.