Synthesis imaging with a lunar orbit array: III. Augmented lagrangian Multiplier Imaging using Gradient descent Optimization (AMIGO)

Abstract

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.