Bayesian Multi-wavelength Imaging of the LMC SN1987A with SRG/eROSITA

TL;DR

This paper introduces a Bayesian imaging algorithm that enhances X-ray images from eROSITA data, enabling detailed analysis of the Large Magellanic Cloud SN1987A by denoising, deconvolving, and decomposing the sky with spectral and spatial correlation modeling.

Contribution

The paper develops a comprehensive Bayesian imaging method incorporating instrumental effects and applies it to eROSITA data of LMC SN1987A, improving image quality and source detection.

Findings

Enhanced image resolution and sensitivity for LMC SN1987A

Successful separation of physical components in X-ray data

Improved calibration for future X-ray observations

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 develop a comprehensive Bayesian imaging algorithm for eROSITA pointing observations. Finally, we apply the developed algorithm to EDR data of the Large Magellanic Cloud (LMC) SN1987A, fusing data sets 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.