通过近端分裂实现射电干涉测量中的并行分面成像（Faceted HyperSARA）：II. 代码与真实数据概念验证
Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): II. Code and real data proof of concept
AI总结：
本文实现并开源 Faceted HyperSARA 的 MATLAB 库，利用分面近端分裂、DDE 建模、校准噪声与降维，在真实 VLA 数据上完成 Cyg A 大规模宽带重建，质量优于 WSClean。
摘要：
在一篇配套论文中，一种名为 Faceted HyperSARA 的射电干涉测量分面宽带成像技术已被提出，并在合成数据上得到验证。Faceted HyperSARA 建立在近期 HyperSARA 方法的基础上，利用底层原始-对偶前向-后向算法固有的分裂功能，将图像重建分解到多个空间-光谱分面上。该方法允许将复杂正则化注入成像过程，同时相较于 HyperSARA 提供额外的并行化灵活性。本文介绍了用于处理真实数据集的新算法功能，这些功能作为在 GitHub 上公开提供的成熟 MATLAB 成像库的一部分实现。文中提出了一个大规模概念验证，以在相较于现有技术的全新数据与参数规模条件下验证 Faceted HyperSARA。研究考虑从 7.4 GB 的 VLA 数据重建天鹅座 A（Cyg A）的 15 GB 宽带图像，在一台 HPC 系统上利用 1440 个 CPU 核心运行约 9 小时。所开展的实验利用新功能来借助已知方向依赖效应（DDEs）以建立测量算子的精确模型，并采用考虑不完美校准的有效噪声水平，展示了所提方法在真实数据上的重建性能。实验还表明，当与进一步的降维功能结合时，Faceted HyperSARA 能够仅使用 91 个 CPU 核心、耗时 39 小时，从相同数据恢复 3.6 GB 的 Cyg A 图像。在该设置下，结果表明所提方法相较于 WSClean 软件中基于宽带 CLEAN 的现有先进算法，可提供更优的重建质量。
英文摘要：
In a companion paper, a faceted wideband imaging technique for radio interferometry, dubbed Faceted HyperSARA, has been introduced and validated on synthetic data. Building on the recent HyperSARA approach, Faceted HyperSARA leverages the splitting functionality inherent to the underlying primal-dual forward-backward algorithm to decompose the image reconstruction over multiple spatio-spectral facets. The approach allows complex regularization to be injected into the imaging process while providing additional parallelization flexibility compared to HyperSARA. The present paper introduces new algorithm functionalities to address real datasets, implemented as part of a fully fledged MATLAB imaging library made available on Github. A large scale proof-of-concept is proposed to validate Faceted HyperSARA in a new data and parameter scale regime, compared to the state-of-the-art. The reconstruction of a 15 GB wideband image of Cyg A from 7.4 GB of VLA data is considered, utilizing 1440 CPU cores on a HPC system for about 9 hours. The conducted experiments illustrate the reconstruction performance of the proposed approach on real data, exploiting new functionalities to leverage known direction-dependent effects (DDEs), for an accurate model of the measurement operator, and an effective noise level accounting for imperfect calibration. They also demonstrate that, when combined with a further dimensionality reduction functionality, Faceted HyperSARA enables the recovery of a 3.6 GB image of Cyg A from the same data using only 91 CPU cores for 39 hours. In this setting, the proposed approach is shown to provide a superior reconstruction quality compared to the state-of-the-art wideband CLEAN-based algorithm of the WSClean software.
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