Speaker
Description
Observation of MeV gamma offers a unique window into nucleosynthesis processes in stellar evolution, radiation from primordial black hole and light dark matter annihilation. However, due to the dominance of Compton scattering and the correspondingly small interaction cross-sections, the detection sensitivity to gammas in MeV region is significantly worse than that in other energy ranges, commonly referred to as the "MeV Gap" problem in the gamma astronomy.
The technology of multi-layer Compton imaging is being developed for next-generation MeV gamma experiments in the world. Supported by the National Key R&D Program of the Ministry of Science and Technology, we are building a multi-layer Compton imaging detector using double-sided silicon strip detector (DSSD) and Cd-Zn-Te (CZT) crystal. This presentation is delivering reconstruction of gamma tracks from the Compton scattering in multiple detector layers based on the simulation. Two image reconstruction algorithms are implemented and their performances are studied: Simple Back Projection (SBP) and List-mode Maximum Likelihood Expectation Maximization (LM-MLEM). The results demonstrate that LM-MLEM achieves substantially superior image quality over SBP, with convergence reached at multiple iterations. Furthermore, we are incorporating deep learning to enhance the imaging accuracy, in particular improve the angular resolution.
| 请选择分会 | 粒子物理实验技术 |
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