利用去卷积改进地面成像的透镜建模
Improving Lens Modelling from Ground-Based Imaging with Deconvolution
- The University of Tokyo（东京大学）
- Inter-University Centre for Astronomy and Astrophysics (IUCAA)（大学间天文学与天体物理学中心）
- Kavli IPMU (WPI), UTokyo Institutes for Advanced Study (UTIAS), The University of Tokyo（理化学研究所 Kavli 宇宙粒子与宇宙学前沿研究所（WPI），东京大学高等研究院（UTIAS），东京大学）
- Institut Teknologi Bandung（万隆理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究提出利用STARRED去卷积算法预处理地面图像，提升分辨率至0.15角秒，从而在HSC模拟透镜系统中实现优于3%精度的关键透镜参数测量，为大规模地面观测样本的精确强透镜建模奠定基础。
AI中文摘要：
强透镜系统的精确质量建模对于揭示宇宙学、星系环境和暗物质本质至关重要。超新星相机-斯巴鲁战略计划（HSC-SSP）已测绘约1000平方度的天空，发现了超过1000个确定或可能的星系尺度引力透镜候选体。由于地面成像的分辨率对精确透镜建模构成挑战，传统上需要高分辨率成像来对这些系统进行详细分析。在本工作中，我们提出应用STARRED算法，该算法利用星小波正则化，作为透镜建模前的预处理步骤，以最大化大型HSC-SSP样本的科学产出。我们在七个具有不同透镜配置的HSC模拟透镜系统上测试了该方法。结果表明，分辨率从约0.6角秒提升至约0.15角秒，使得地面图像中关键透镜参数（如爱因斯坦半径和质量轴比）的测量精度得到提高，且精度优于3%。这些结果为从大量地面观测样本中精确测量关键强透镜量铺平了道路。
英文摘要：
Accurate and precise mass modelling of strong lensing systems is essential for extracting insights into cosmology, galaxy environments, and the nature of dark matter. The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP), which has mapped $\sim$1000 square degrees of the sky, discovered over 1000 definite or probable galaxy-scale gravitational lens candidates. As the resolution of ground-based imaging poses challenges for accurate lens modelling, high-resolution imaging is traditionally sought after for detailed analyses of these systems. In this work, we instead propose applying the STARRED algorithm, which leverages starlet wavelet regularisation, as a preprocessing step before lens modelling to maximise the scientific output of the large HSC-SSP sample. We test this approach on seven HSC-like mock lensing systems with different lensing configurations. Our results show that the resolution boost from $\sim$0.6" to $\sim$0.15" enables improved accuracy and sub-3% precision measurements of key lensing parameters such as the Einstein radius and the mass axis ratio for ground-based images. These results pave the way for accurate measurements of key strong lensing quantities from a large sample of ground-based observations.
