Imaging MeV Gamma-ray Lines with Advanced Image Reconstruction Framework for COSI

The Compton Spectrometer and Imager (COSI), scheduled for launch in 2027, will improve sensitivity for MeV gamma-ray line observations by an order of magnitude, opening new windows to study cosmic nucleosynthesis. Primary targets include the 511 keV emission from positron annihilation, $^{26}$Al from stellar nucleosynthesis, and $^{44}$Ti from supernova remnants. However, image reconstruction in this energy band presents unique challenges: Compton telescopes constrain gamma-ray directions to circles rather than points, necessitating statistical approaches for source recovery. Additionally, complex background environments require careful treatment for reliable source detection. We present an advanced image reconstruction framework implemented within the COSIpy data analysis library. Our approach extends COSIpy's capabilities by introducing a modified Richardson-Lucy algorithm with Bayesian priors optimized for MeV gamma-ray observations. The framework flexibly incorporates prior distributions for sparseness and smoothness while simultaneously optimizing background components. The modular design enables multi-dataset handling, multi-energy reconstruction, and unbinned likelihood analysis, providing a comprehensive tool for Compton telescope data analysis. We evaluate the method using simulated three-month COSI observations of key nuclear lines ($^{44}$Ti at 1.157 MeV, $^{26}$Al at 1.809 MeV, and positron annihilation at 0.511 MeV). Our results demonstrate successful suppression of artifacts in point source reconstructions while preserving extended emission features. For the complex 511 keV morphology, our method achieves total flux accuracy within 10\% of the true value, while conventional approaches overestimate by $\sim$60\% due to artifact amplification. This work establishes a robust foundation for MeV gamma-ray image analysis with COSI and represents an important step toward advancing our understanding of nucleosynthesis, positron annihilation, and other high-energy phenomena in future missions.

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Publication Details

Published
2025-09-24
DOI
https://doi.org/10.22323/1.501.0891
Primary Topic
Particle Detector Development and Performance
Type
article
Field-Weighted Citation Impact
0.00

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article

Imaging MeV Gamma-ray Lines with Advanced Image Reconstruction Framework for COSI

Chien-You Huang, Thomas Siegert, Shigeki Matsumoto, Carolyn Kierans et al.
Particle Detector Development and Performance
article

Imaging MeV Gamma-ray Lines with Advanced Image Reconstruction Framework for COSI

Chien-You Huang, Thomas Siegert, Shigeki Matsumoto, Carolyn Kierans, Saurabh Mittal, Sean N. Pike, Eliza Neights, F. Rogers, Savitri Gallego, Michela Negro, Christopher M. Karwin, Andreas Zoglauer, Keigo Okuma, Israel Martinez-Castellanos, Alyson Joens, Yong Sheng, Jarred Roberts, Dieter H. Hartmann, Yu Watanabe, John Tomsick, Uwe Oberlack, Hiroki Yoneda, Anaya Valluvan, Steven Boggs, Tadayuki Takahashi, Hugh Bates
article en

Abstract

The Compton Spectrometer and Imager (COSI), scheduled for launch in 2027, will improve sensitivity for MeV gamma-ray line observations by an order of magnitude, opening new windows to study cosmic nucleosynthesis. Primary targets include the 511 keV emission from positron annihilation, $^{26}$Al from stellar nucleosynthesis, and $^{44}$Ti from supernova remnants. However, image reconstruction in this energy band presents unique challenges: Compton telescopes constrain gamma-ray directions to circles rather than points, necessitating statistical approaches for source recovery. Additionally, complex background environments require careful treatment for reliable source detection. We present an advanced image reconstruction framework implemented within the COSIpy data analysis library. Our approach extends COSIpy's capabilities by introducing a modified Richardson-Lucy algorithm with Bayesian priors optimized for MeV gamma-ray observations. The framework flexibly incorporates prior distributions for sparseness and smoothness while simultaneously optimizing background components. The modular design enables multi-dataset handling, multi-energy reconstruction, and unbinned likelihood analysis, providing a comprehensive tool for Compton telescope data analysis. We evaluate the method using simulated three-month COSI observations of key nuclear lines ($^{44}$Ti at 1.157 MeV, $^{26}$Al at 1.809 MeV, and positron annihilation at 0.511 MeV). Our results demonstrate successful suppression of artifacts in point source reconstructions while preserving extended emission features. For the complex 511 keV morphology, our method achieves total flux accuracy within 10\% of the true value, while conventional approaches overestimate by $\sim$60\% due to artifact amplification. This work establishes a robust foundation for MeV gamma-ray image analysis with COSI and represents an important step toward advancing our understanding of nucleosynthesis, positron annihilation, and other high-energy phenomena in future missions.

National Aeronautics and Space Administration, Ames Research Center, Goddard Space Flight Center, Japan Society for the Promotion of Science
Openalex Percentile: Top 89%
Particle Detector Development and Performance
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