First spatio-spectral Bayesian imaging of SN1006 in X-ray
Margret Westerkamp, Vincent Eberle, Matteo Guardiani, Philipp Frank,, Lukas Platz, Philipp Arras, Jakob Knollm\"uller, Julia Stadler, Torsten, En{\ss}lin

TL;DR
This paper presents a novel Bayesian imaging method that reconstructs and decomposes X-ray images of SN1006 into diffuse and point-like components, improving resolution and uncertainty quantification for supernova remnant analysis.
Contribution
It introduces a spatio-spectral Bayesian reconstruction technique that effectively decomposes supernova remnant images into different emission components using prior spatial and spectral correlations.
Findings
Reconstructed detailed, denoised images of SN1006.
Successfully decomposed emission into diffuse and point sources.
Enhanced understanding of small-scale structures in the remnant.
Abstract
Supernovae are an important source of energy in the interstellar medium. Young remnants of supernovae have a peak emission in the X-ray region, making them interesting objects for X-ray observations. In particular, the supernova remnant SN1006 is of great interest due to its historical record, proximity and brightness. It has therefore been studied by several X-ray telescopes. Improving the X-ray imaging of this and other remnants is important but challenging as it requires to address a spatially varying instrument response in order to achieve a high signal-to-noise ratio. Here, we use Chandra observations to demonstrate the capabilities of Bayesian image reconstruction using information field theory. Our objective is to reconstruct denoised, deconvolved and spatio-spectral resolved images from X-ray observations and to decompose the emission into different morphologies, namely diffuse…
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Taxonomy
TopicsGamma-ray bursts and supernovae · Nuclear Physics and Applications · Advanced MRI Techniques and Applications
