Abstract
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.
Abstract (translated)
无线电干涉测量通过合成大型有效孔径并解决去卷积问题来实现对天文射电源的高分辨率成像，从而利用一组天线阵列。深度学习已成为解决成像问题的一项有前景的技术，它能降低计算成本，并支持超分辨率成像。然而，现有的基于深度学习的方法在处理高动态范围、大视场以及训练和测试条件不匹配等问题时往往表现不足，难以满足实际应用的要求。 在此工作中，我们建立并扩展了POLISH框架——这是最近提出的一种用于无线电干涉测量成像的深度学习模型。我们引入了一些关键改进，旨在实现在现实世界条件下稳健重建和超分辨率：（1）一种基于补丁训练和拼接策略的方法，以适应宽视野成像；（2）一种非线性的arcsinh强度变换方法来管理高动态范围。 使用带有真实天空模型和点扩散函数(PSF)的T-RECS模拟套件进行了全面评估。我们证明了我们的方法显著提高了重建质量和鲁棒性。我们在现实世界的强引力透镜模拟上测试了该模型，并展示了在去卷积后，利用我们的POLISH模型可以恢复出接近PSF尺度的爱因斯坦半径的透镜系统，从而有可能从深巡天阵列(DSA)调查中获得比使用图像平面CLEAN多10倍的星系-星系透镜系统。 我们的结果强调了深度学习模型作为下一代射电天文领域实用、可扩展工具的巨大潜力。
URL
https://arxiv.org/abs/2603.09162
