A sky model from CLEAN deconvolution is a particularly effective high dynamic range reconstruction in radio astronomy, which can effectively model the sky and remove the sidelobes of the point spread function (PSF) caused by incomplete sampling in the spatial frequency domain. Compared to scale-free and multi-scale sky models, adaptive-scale sky modeling, which can model both compact and diffuse features, has been proven to have better sky modeling capabilities in narrowband simulated data, especially for large-scale features in high-sensitivity observations which are exactly one of the challenges of data processing for the Square Kilometre Array (SKA). However, adaptive scale CLEAN algorithms have not been verified by real observation data and allow negative components in the model. In this paper, we propose an adaptive scale model algorithm with non-negative constraint and wideband imaging capacities, and it is applied to simulated SKA data and real observation data from the Karl G. Jansky Very Large Array (JVLA), an SKA precursor. Experiments show that the new algorithm can reconstruct more physical models with rich details. This work is a step forward for future SKA image reconstruction and developing SKA imaging pipelines.

来自 CLEAN 反卷积的天空模型是射电天文中一种特别有效的高动态范围重建，它可以有效地对天空进行建模并去除空间频域中不完全采样引起的点扩散函数 (PSF) 的旁瓣。与无尺度和多尺度天空模型相比，自适应尺度天空建模既可以对紧凑特征也可以对漫反射特征进行建模，已被证明在窄带模拟数据中具有更好的天空建模能力，尤其是对于高空域的大尺度特征。灵敏度观测正是平方公里阵列 (SKA) 数据处理的挑战之一。然而，自适应尺度 CLEAN 算法尚未得到实际观测数据的验证，并且模型中存在负分量。在本文中，我们提出了一种具有非负约束和宽带成像能力的自适应尺度模型算法，并将其应用于模拟的 SKA 数据和来自 SKA 前身 Karl G. Jansky 超大阵列 (JVLA) 的真实观测数据。实验表明，新算法可以重构出更多细节丰富的物理模型。这项工作是未来 SKA 图像重建和开发 SKA 成像管道的一步。