Enhancing Compton telescope imaging with maximum a posteriori estimation

A modified Richardson–Lucy algorithm for the Compton Spectrometer and Imager

¹ Julius-Maximilians-Universität Würzburg, Fakultät für Physik und Astronomie, Institut für Theoretische Physik und Astrophysik, Lehrstuhl für Astronomie, Emil-Fischer-Str. 31, 97074 Würzburg, Germany
² RIKEN Nishina Center, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
³ Department of Astronomy, University of Maryland, College Park, MD 20742, USA
⁴ NASA Goddard Space Flight Center, 8800 Greenbelt Road, Greenbelt, MD 20771, USA
⁵ Institut für Physik & Exzellenzcluster PRISMA+, Johannes Gutenberg-Universität Mainz, 55099 Mainz, Germany
⁶ Department of Physics and Astronomy, Clemson University, Clemson, SC 29634, USA
⁷ Department of Astronomy & Astrophysics, UC San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
⁸ Institute of Astronomy, National Tsing Hua University, Guangfu Rd., Hsinchu City 300044, Taiwan
⁹ Space Sciences Laboratory, UC Berkeley, University of California, 7 Gauss Way, Berkeley, CA 94720, USA
¹⁰ Kavli Institute for the Physics and Mathematics of the Universe (WPI), The University of Tokyo Institutes for Advanced Study, The University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Chiba 277-8583, Japan
¹¹ The George Washington University, Department of Physics, 725 21st St NW, Washington, DC 20052, USA
¹² Louisiana State University, BatonRouge, LA 70803, USA
¹³ Kobayashi-Masukawa Institute, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 4648602, Japan

^⋆ Corresponding author: hiroki.yoneda.phys@gmail.com

Received: 19 December 2024
Accepted: 31 March 2025

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 ⁴⁴Ti (1.157 MeV), ²⁶Al (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.