Bayesian multiband imaging of SN1987A in the Large Magellanic Cloud with SRG/eROSITA

¹ Max Planck Institute for Astrophysics, Karl-Schwarzschild-Straße 1, 85748 Garching, Germany
² Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, 80539 Munich, Germany
³ Kavli Institute for Particle Astrophysics & Cosmology, Stanford University, Stanford, CA 94305, USA
⁴ Max Planck Institute for Extraterrestrial Physics, Gießenbachstraße 1, 85748 Garching, Germany
⁵ Excellence Cluster ORIGINS, Boltzmannstraße 2, 85748 Garching, Germany

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Received: 18 October 2024
Accepted: 27 September 2025

Abstract

The eROSITA Early Data Release (EDR) and eROSITA All-Sky Survey (eRASS1) data have already revealed a remarkable number of undiscovered X-ray sources. Using Bayesian inference and generative modeling techniques for X-ray imaging, we aim to increase the sensitivity and scientific value of these observations by denoising, deconvolving, and decomposing the X-ray sky. Leveraging information field theory, we can exploit the spatial and spectral correlation structures of the different physical components of the sky with non-parametric priors to enhance the image reconstruction. By incorporating instrumental effects into the forward model, we developed a comprehensive Bayesian imaging algorithm for eROSITA pointing observations. Finally, we applied the developed algorithm to EDR data of the Large Magellanic Cloud (LMC) SN1987A, fusing datasets from observations made by five different telescope modules. The final result is a denoised, deconvolved, and decomposed view of the LMC, which enables the analysis of its fine-scale structures, the identification of point sources in this region, and enhanced calibration for future work.