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

The EDR and 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 th...

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Hauptverfasser: Eberle, Vincent, Guardiani, Matteo, Westerkamp, Margret, Frank, Philipp, Freyberg, Michael, Salvato, Mara, Enßlin, Torsten
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Guardiani, Matteo
Westerkamp, Margret
Frank, Philipp
Freyberg, Michael
Salvato, Mara
Enßlin, Torsten
description The EDR and 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 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 creation of point source catalogues of this region, and enhanced calibration for future work.
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subjects Computer Science - Information Theory
Mathematics - Information Theory
Physics - Data Analysis, Statistics and Probability
Physics - High Energy Astrophysical Phenomena
Physics - Instrumentation and Methods for Astrophysics
title Bayesian Multi-wavelength Imaging of the LMC SN1987A with SRG/eROSITA
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