Multicomponent imaging of the Fermi gamma-ray sky in the spatio-spectral domain.
Astron. Astrophys. 680:30 (2023)
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.
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Publication type
Article: Journal article
Document type
Scientific Article
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Keywords
Gamma Rays: Diffuse Background ; Gamma Rays: General ; Gamma Rays: Ism ; Methods: Data Analysis ; Methods: Statistical; Large-area Telescope; Source-count Distribution; Information Field-theory; Galactic-center Excess; Photon Observations; Lat Observations; Dark-matter; Search; Emission; Morphology
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Language
english
Publication Year
2023
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0
HGF-reported in Year
2023
ISSN (print) / ISBN
0004-6361
e-ISSN
1432-0746
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Volume: 680,
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Article Number: 30
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EDP Sciences
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17, Ave Du Hoggar, Pa Courtaboeuf, Bp 112, F-91944 Les Ulis Cedex A, France
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Peer reviewed
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-505500-001
G-503800-001
Grants
German Federal Ministry of Education and Research (BMBF)
Deutsche Forschungsgemeinschaft (DFG, German Research foundation)
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Erfassungsdatum
2024-01-10