天空的 polishing：基于宽视场和高动态范围干涉成像的图像重建及其在强引力透镜发现中的应用
POLISH'ing the Sky: Wide-Field and High-Dynamic Range Interferometric Image Reconstruction with Application to Strong Lens Discovery
AI总结：
本文提出改进的POLISH框架，通过补丁训练和非线性强度变换实现宽视场和高动态范围的干涉成像，提升强引力透镜发现的重建质量与鲁棒性。
摘要：
射电干涉成像通过合成一个大型有效孔径，利用天线阵列和求解去卷积问题来实现对天文射电源的高分辨率成像。深度学习已出现作为成像问题的有希望的解决方案，减少了计算成本，并实现了超分辨率。然而，现有的基于深度学习的方法由于在处理高动态范围、大视场和训练与测试条件不匹配方面存在限制，难以满足实际部署的需求。在本工作中，我们基于并扩展了POLISH框架，这是一种最近的深度学习模型，用于射电干涉成像。我们引入了关键的改进，以在实际条件下实现稳健的重建和超分辨率：（1）一种基于补丁的训练和拼接策略，用于扩展到宽视场成像；（2）一种基于非线性arcsinh的强度变换，用于管理高动态范围。我们使用T-RECS模拟套件进行了全面的评估，使用现实的天空模型和点扩散函数（PSF），并证明我们的方法显著提高了重建质量和鲁棒性。我们测试了模型在现实模拟的强引力透镜上的表现，并显示具有Einstein半径接近PSF尺度的透镜系统在使用我们的POLISH模型去卷积后可以被恢复，这可能使深合成阵列（DSA）调查从图像平面CLEAN中获得10倍更多的星系-星系透镜系统。我们的结果突显了深度学习模型作为下一代射电天文学实用、可扩展工具的潜力。
英文摘要：
Radio interferometry enables high-resolution imaging of astronomical radio sources by synthesizing a large effective aperture from an array of antennas and solving a deconvolution problem to reconstruct the image. Deep learning has emerged as a promising solution to the imaging problem, reducing computational costs and enabling super-resolution. However, existing DL-based methods often fall short of the requirements for real-world deployment due to limitations in handling high dynamic range, large field of view, and mismatches between training and test conditions. In this work, we build upon and extend the POLISH framework, a recent DL model for radio interferometric imaging. We introduce key improvements to enable robust reconstruction and super-resolution under real-world conditions: (1) a patch-wise training and stitching strategy for scaling to wide-field imaging and (2) a nonlinear arcsinh-based intensity transformation to manage high dynamic range. We conduct comprehensive evaluations using the T-RECS simulation suite with realistic sky models and point spead functions (PSF), and demonstrate that our approach significantly improves reconstruction quality and robustness. We test the model on realistic simulated strong gravitational lenses and show that lens systems with Einstein radii near the PSF scale can be recovered after deconvolution with our POLISH model, potentially yielding 10$\times$ more galaxy-galaxy lensing systems from the Deep Synoptic Array (DSA) survey than with image-plane CLEAN. Our results highlight the potential of DL models as practical, scalable tools for next-generation radio astronomy.
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