Bayesian Multi-wavelength Imaging of the LMC SN1987A with SRG/eROSITA
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910031763472384 |
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| author | Eberle, Vincent Guardiani, Matteo Westerkamp, Margret Frank, Philipp Freyberg, Michael Salvato, Mara Enßlin, Torsten |
| author_facet | Eberle, Vincent Guardiani, Matteo Westerkamp, Margret Frank, Philipp Freyberg, Michael Salvato, Mara Enßlin, Torsten |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_14599 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bayesian Multi-wavelength Imaging of the LMC SN1987A with SRG/eROSITA Eberle, Vincent Guardiani, Matteo Westerkamp, Margret Frank, Philipp Freyberg, Michael Salvato, Mara Enßlin, Torsten Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Information Theory Data Analysis, Statistics and Probability 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. |
| title | Bayesian Multi-wavelength Imaging of the LMC SN1987A with SRG/eROSITA |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena Information Theory Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2410.14599 |