月球轨道阵列的合成成像：III. 使用梯度下降优化的增广拉格朗日乘子成像（AMIGO）
Synthesis imaging with a lunar orbit array: III. Augmented lagrangian Multiplier Imaging using Gradient descent Optimization (AMIGO)
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中文总结 AI 辅助
针对月球轨道阵列成像面临的挑战，提出AMIGO算法，结合小批量梯度下降与增广拉格朗日乘子技术，降低计算成本并纳入物理先验，经模拟数据验证，为月球轨道阵列全天空成像提供可行框架。
中文摘要 AI 辅助
低于30MHz的地面射电观测受到电离层干扰和来自地球的射频干扰（RFI）的严重限制。已提出一项月球轨道射电干涉测量任务——最长波长探天（DSL，中文名“鸿蒙”）来克服这些障碍。然而，对于这样的任务存在新挑战，如几乎全天空视场和动态三维基线，这需要巨大计算成本用于干涉图像重建。在这项工作中，我们提出了AMIGO（使用梯度下降优化的增广拉格朗日乘子成像），一种适用于像DSL这样的月球轨道阵列的新型成像算法，它将小批量梯度下降（MBGD）方法与增广拉格朗日乘子（ALM）技术相结合。MBGD降低了计算复杂度和内存成本，能有效处理大型数据集。ALM将诸如非负天空温度和先验角功率谱等物理先验灵活纳入成像算法，并有可调停止准则来定量控制先验强度。我们使用在实际DSL轨道配置下生成的模拟可见度数据验证了AMIGO。不同频率和空间分辨率下重建的天空图表明，该方法为月球轨道阵列全天空成像提供了一个计算上可行的框架。
英文摘要
Ground-based radio observations below 30 MHz are severely limited by ionospheric interference and radio frequency interference (RFI) from Earth. A lunar-orbiting radio interferometer mission, the Discovering the Sky at the Longest wavelength (DSL, also known by its Chinese name ``Hongmeng''), has been proposed to overcome these obstacles. However, for such a mission, there are new challenges, such as the nearly all-sky field of view and dynamic 3D baselines, which require a huge computational cost for interferometric image reconstruction. In this work, we present AMIGO (Augmented lagrangian Multiplier Imaging using Gradient descent Optimization), a novel imaging algorithm tailored to lunar-orbiting arrays like DSL, combining the Mini-Batch Gradient Descent (MBGD) method with the Augmented Lagrangian Multiplier (ALM) technique. MBGD reduces the computational complexity and memory cost, enabling efficient handling of large datasets. ALM flexibly incorporates physical priors like non-negative sky temperature and prior angular power spectrum into the imaging algorithm, with adjustable stopping criteria to quantitatively control prior strength. We validate AMIGO using mock visibility data generated under realistic DSL orbit configurations. Reconstructed sky maps at various frequencies and spatial resolutions show that this approach provides a computationally feasible framework for all-sky imaging with a lunar-orbiting array.
