Kavli Affiliate: Shigeki Matsumoto
| First 5 Authors: Hiroki Yoneda, Thomas Siegert, Israel Martinez-Castellanos, Savitri Gallego, Chris Karwin
| Summary:
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
towards establishing a robust data analysis for studying nucleosynthesis,
positron annihilation, and other high-energy phenomena in our Galaxy.
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