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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creator | Eberle, Vincent 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. |
doi_str_mv | 10.48550/arxiv.2410.14599 |
format | Article |
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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.</description><identifier>DOI: 10.48550/arxiv.2410.14599</identifier><language>eng</language><subject>Computer Science - Information Theory ; Mathematics - Information Theory ; Physics - Data Analysis, Statistics and Probability ; Physics - High Energy Astrophysical Phenomena ; Physics - Instrumentation and Methods for Astrophysics</subject><creationdate>2024-10</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2410.14599$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2410.14599$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Eberle, Vincent</creatorcontrib><creatorcontrib>Guardiani, Matteo</creatorcontrib><creatorcontrib>Westerkamp, Margret</creatorcontrib><creatorcontrib>Frank, Philipp</creatorcontrib><creatorcontrib>Freyberg, Michael</creatorcontrib><creatorcontrib>Salvato, Mara</creatorcontrib><creatorcontrib>Enßlin, Torsten</creatorcontrib><title>Bayesian Multi-wavelength Imaging of the LMC SN1987A with SRG/eROSITA</title><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.</description><subject>Computer Science - Information Theory</subject><subject>Mathematics - Information Theory</subject><subject>Physics - Data Analysis, Statistics and Probability</subject><subject>Physics - High Energy Astrophysical Phenomena</subject><subject>Physics - Instrumentation and Methods for Astrophysics</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNpjYJA0NNAzsTA1NdBPLKrILNMzMgEKGJqYWlpyMrg6JVamFmcm5in4luaUZOqWJ5al5qTmpZdkKHjmJqZn5qUr5KcplGSkKvj4OisE-xlaWpg7KpRnAuWDg9z1U4P8gz1DHHkYWNMSc4pTeaE0N4O8m2uIs4cu2ML4gqLM3MSiyniQxfFgi40JqwAAKFA18g</recordid><startdate>20241018</startdate><enddate>20241018</enddate><creator>Eberle, Vincent</creator><creator>Guardiani, Matteo</creator><creator>Westerkamp, Margret</creator><creator>Frank, Philipp</creator><creator>Freyberg, Michael</creator><creator>Salvato, Mara</creator><creator>Enßlin, Torsten</creator><scope>AKY</scope><scope>AKZ</scope><scope>GOX</scope></search><sort><creationdate>20241018</creationdate><title>Bayesian Multi-wavelength Imaging of the LMC SN1987A with SRG/eROSITA</title><author>Eberle, Vincent ; Guardiani, Matteo ; Westerkamp, Margret ; Frank, Philipp ; Freyberg, Michael ; Salvato, Mara ; Enßlin, Torsten</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2410_145993</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Information Theory</topic><topic>Mathematics - Information Theory</topic><topic>Physics - Data Analysis, Statistics and Probability</topic><topic>Physics - High Energy Astrophysical Phenomena</topic><topic>Physics - Instrumentation and Methods for Astrophysics</topic><toplevel>online_resources</toplevel><creatorcontrib>Eberle, Vincent</creatorcontrib><creatorcontrib>Guardiani, Matteo</creatorcontrib><creatorcontrib>Westerkamp, Margret</creatorcontrib><creatorcontrib>Frank, Philipp</creatorcontrib><creatorcontrib>Freyberg, Michael</creatorcontrib><creatorcontrib>Salvato, Mara</creatorcontrib><creatorcontrib>Enßlin, Torsten</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv Mathematics</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Eberle, Vincent</au><au>Guardiani, Matteo</au><au>Westerkamp, Margret</au><au>Frank, Philipp</au><au>Freyberg, Michael</au><au>Salvato, Mara</au><au>Enßlin, Torsten</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Bayesian Multi-wavelength Imaging of the LMC SN1987A with SRG/eROSITA</atitle><date>2024-10-18</date><risdate>2024</risdate><abstract>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.</abstract><doi>10.48550/arxiv.2410.14599</doi><oa>free_for_read</oa></addata></record> |
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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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