通过近端分裂实现射电干涉测量中的并行分面成像（Faceted HyperSARA）：I. 算法与模拟
Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): I. Algorithm and simulations
AI总结：
本文提出 Faceted HyperSARA，利用近端分裂将宽带图像立方体按空间-光谱分面并行处理正则化项，并给出自动参数设置，模拟证实其相较 HyperSARA 显著提升并行能力。
摘要：
即将建成的射电干涉仪旨在以新的分辨率和灵敏度水平对天空成像，对 SKA 而言，其宽带图像立方体将接近 PB 级。现代近端优化算法已显示出显著优于 CLEAN 的潜力，这得益于它们能够引入复杂图像模型，以正则化由可见度数据进行图像重建的逆问题。研究还表明，这些算法可在大规模数据上实现并行化：借助分裂功能将数据分解为多个块，从而并行处理目标函数中各块特有的数据保真度项。本文聚焦强度成像，进一步利用该分裂功能将图像立方体分解为空间-光谱分面，并支持并行处理目标函数中各分面特有的正则化项，由此提出“Faceted HyperSARA”算法。文中还提出了可靠的启发式方法，可基于从可见度域转移到正则化应用域的噪声水平估计，自动设置目标函数中的正则化参数。基于 MATLAB 实现、使用接近 GB 规模的合成图像立方体和数据得到的模拟结果证实：与非分面方法（HyperSARA）相比，分面能够大幅提升并行化能力。
英文摘要：
Upcoming radio interferometers are aiming to image the sky at new levels of resolution and sensitivity, with wide-band image cubes reaching close to the Petabyte scale for SKA. Modern proximal optimization algorithms have shown a potential to significantly outperform CLEAN thanks to their ability to inject complex image models to regularize the inverse problem for image formation from visibility data. They were also shown to be parallelizable over large data volumes thanks to a splitting functionality enabling the decomposition of the data into blocks, for parallel processing of block-specific data-fidelity terms involved in the objective function. Focusing on intensity imaging, the splitting functionality is further exploited in this work to decompose the image cube into spatio-spectral facets, and enable parallel processing of facet-specific regularization terms in the objective function, leading to the "Faceted HyperSARA" algorithm. Reliable heuristics enabling an automatic setting of the regularization parameters involved in the objective are also introduced, based on estimates of the noise level, transferred from the visibility domain to the domains where the regularization is applied. Simulation results based on a MATLAB implementation and involving synthetic image cubes and data close to Gigabyte size confirm that faceting can provide a major increase in parallelization capability when compared to the non-faceted approach (HyperSARA).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
