Speaker
Description
For a deep understanding of the X-ray universe, it is crucial to rely on complete and accurate information on its primary constituents. These constituents, such as active galactic nuclei, galaxies, and other compact and diffuse objects display distinct features in the sky and therefore imprint differently on astronomical data. In this work, we leverage these differences to construct statistical models for their a priori independent spatial and spectral distributions in the sky. This not only enhances the overall observation reconstruction, but also allows to segregate the flux of the various components that populate the sky and more accurately study their individual features. Specifically, we introduce a new technique that uses a notion of model stress to automatically detect and separate point-like sources from diffuse, correlated structures. We showcase the benefits of this approach on publicly available eROSITA data.
