Enhancing Compton telescope imaging with maximum a posteriori estimation

We present a modified Richardson-Lucy (RL) algorithm tailored for image reconstruction in MeV gamma-ray observations, focusing on its application to the upcoming Compton Spectrometer and Imager (COSI) mission. Our method addresses key challenges in MeV gamma-ray astronomy by incorporating Bayesian priors for sparseness and smoothness while optimizing background components simultaneously. We introduce a novel sparsity term suitable for Poisson-sampled data in addition to a smoothness prior, allowing for flexible reconstruction of both point sources and extended emission. The performance of the algorithm is evaluated using simulated three-month COSI observations of gamma-ray lines of 44 Ti (1.157 MeV), 26 Al (1.809 MeV), and positron annihilation (0.511 MeV), respectively, representing various spatial features. Our results demonstrate significant improvements over conventional RL methods, particularly in suppressing artificial structures in point source reconstructions and retaining diffuse spatial structures. This work represents an important step toward establishing a robust data analysis for studying nucleosynthesis, positron annihilation, and other high-energy phenomena in our Galaxy.

Authors

Publication Details

Journal
Astronomy and Astrophysics
Published
2025-04-08
DOI
https://doi.org/10.1051/0004-6361/202453528
Citations
1
Primary Topic
Medical Imaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
1.36

Funders

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article

Enhancing Compton telescope imaging with maximum a posteriori estimation

John A. Tomsick, Kusuo OKUMA, Thomas Siegert, Steven E. Boggs et al.
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Astronomy and Astrophysics
Medical Imaging Techniques and Applications
1.36
article

Enhancing Compton telescope imaging with maximum a posteriori estimation

John A. Tomsick, Kusuo OKUMA, Thomas Siegert, Steven E. Boggs, Shigeki Matsumoto, Carolyn Kierans, Saurabh Mittal, Hiroki Yoneda, Sean N. Pike, Tadayuki Takahashi, Henry H. Bates, Can Huang, D. H. Hartmann, Eliza Neights, F. Rogers, Michela Negro, Christopher M. Karwin, Andreas Zoglauer, Alyson Joens, S. Gallego, U. Oberlack, Yu Watanabe, Yong Sheng, Jarred Roberts, I. Martinez-Castellanos, Anaya Valluvan
article en
1 citations

Abstract

We present a modified Richardson-Lucy (RL) algorithm tailored for image reconstruction in MeV gamma-ray observations, focusing on its application to the upcoming Compton Spectrometer and Imager (COSI) mission. Our method addresses key challenges in MeV gamma-ray astronomy by incorporating Bayesian priors for sparseness and smoothness while optimizing background components simultaneously. We introduce a novel sparsity term suitable for Poisson-sampled data in addition to a smoothness prior, allowing for flexible reconstruction of both point sources and extended emission. The performance of the algorithm is evaluated using simulated three-month COSI observations of gamma-ray lines of 44 Ti (1.157 MeV), 26 Al (1.809 MeV), and positron annihilation (0.511 MeV), respectively, representing various spatial features. Our results demonstrate significant improvements over conventional RL methods, particularly in suppressing artificial structures in point source reconstructions and retaining diffuse spatial structures. This work represents an important step toward establishing a robust data analysis for studying nucleosynthesis, positron annihilation, and other high-energy phenomena in our Galaxy.

Astronomy and AstrophysicsVol. 697
National Aeronautics and Space Administration, Ministry of Education, Culture, Sports, Science and Technology, Deutsches Zentrum für Luft- und Raumfahrt, Johannes Gutenberg-Universität Mainz, Ames Research Center, Goddard Space Flight Center, Japan Society for the Promotion of Science
Openalex Percentile: Top 22%
Medical Imaging Techniques and Applications
1.36
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