Abstract
The gamma-ray sky as seen by the Large Area Telescope (LAT) on board the Fermi satellite is a superposition of emissions from many processes. To study them, a rich toolkit of analysis methods for gamma-ray observations has been developed, most of which rely on emission templates to model foreground emissions. Here, we aim to complement these methods by presenting a template-free spatio-spectral imaging approach for the gamma-ray sky, based on a phenomenological modeling of its emission components. It is formulated in a Bayesian variational inference framework and allows a simultaneous reconstruction and decomposition of the sky into multiple emission components, enabled by a self-consistent inference of their spatial and spectral correlation structures. Additionally, we formulated the extension of our imaging approach to template-informed imaging, which includes adding emission templates to our component models while retaining the “data-drivenness” of the reconstruction. We demonstrate the performance of the presented approach on the ten-year Fermi LAT data set. With both template-free and template-informed imaging, we achieve a high quality of fit and show a good agreement of our diffuse emission reconstructions with the current diffuse emission model published by the Fermi Collaboration. We quantitatively analyze the obtained data-driven reconstructions and critically evaluate the performance of our models, highlighting strengths, weaknesses, and potential improvements. All reconstructions have been released as data products.
Funder
Deutsche Forschungsgemeinschaft
German Federal Ministry of Education and Research
Subject
Space and Planetary Science,Astronomy and Astrophysics