论文标题
第一个用于射电天文学中深层超分辨率宽场成像的AI：ESO 137--006中的揭幕结构
First AI for deep super-resolution wide-field imaging in radio astronomy: unveiling structure in ESO 137--006
论文作者
论文摘要
我们介绍了第一个基于AI的基于AI的框架，用于深，超分辨率，宽场射击图像，并在ESO〜137-006射线星系的观察结果上演示。解决图像重建的逆问题的算法框架建立在最新的``插件''方案上，即在优化算法中将其作为图像常规符将其作为图像正规算法注入，该算法在优化算法中替代直至在DeNoising Steps and deNoising Steps和梯度数据之间的收敛性和梯度数据之间的收敛。我们研究了高分辨率高动力范围Deoisiser的手工制作和学习的变体。我们提出了一种并行算法实现，该实现依靠图像的自动分解为方面，将测量运算符分为稀疏的低维块，从而可以可扩展到大数据和图像尺寸。我们在包含ESO〜137-006的广阔视野中验证了图像形成的框架，从1053和1399 MHz的Meerkat数据的19 GB。恢复的地图比清洁的分辨率和动态范围要大得多，从而揭示了靠近银河系芯的准直线线。
We introduce the first AI-based framework for deep, super-resolution, wide-field radio-interferometric imaging, and demonstrate it on observations of the ESO~137-006 radio galaxy. The algorithmic framework to solve the inverse problem for image reconstruction builds on a recent ``plug-and-play'' scheme whereby a denoising operator is injected as an image regulariser in an optimisation algorithm, which alternates until convergence between denoising steps and gradient-descent data-fidelity steps. We investigate handcrafted and learned variants of high-resolution high-dynamic range denoisers. We propose a parallel algorithm implementation relying on automated decompositions of the image into facets and the measurement operator into sparse low-dimensional blocks, enabling scalability to large data and image dimensions. We validate our framework for image formation at a wide field of view containing ESO~137-006, from 19 gigabytes of MeerKAT data at 1053 and 1399 MHz. The recovered maps exhibit significantly more resolution and dynamic range than CLEAN, revealing collimated synchrotron threads close to the galactic core.
