Multicomponent imaging of the Fermi gamma-ray sky in the spatio-spectral domain^★

¹ Max Planck Institute for Astrophysics, Karl-Schwarzschild-Str. 1, 85748 Garching, Germany
e-mail: lplatz@mpa-garching.mpg.de, ensslin@mpa-garching.mpg.de
² Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, 80539 Munich, Germany
³ Institute of Biological and Medical Imaging, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
⁴ Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
⁵ Technical University of Munich; School of Medicine, Chair of Biological Imaging at the Central Institute for Translational Cancer Research (TranslaTUM), Einsteinstraße 25, 81675 Munich, Germany
⁶ Technical University of Munich; School of Natural Sciences, Chair for Data Science in Physics, Boltzmannstraße 2, 85748 Garching, Germany
⁷ Excellence Cluster ORIGINS, Boltzmannstraße 2, 85748 Garching, Germany
⁸ Technical University of Munich; School of Computation, Information and Technology, Boltzmannstr. 3, 85748 Garching, Germany

Received: 19 April 2022
Accepted: 5 August 2023

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.