First AI for Deep Super-resolution Wide-field Imaging in Radio Astronomy: Unveiling Structure in ESO 137-006
First AI for Deep Super-resolution Wide-field Imaging in Radio Astronomy: Unveiling Structure in ESO 137-006
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DOI：
10.3847/2041-8213/ac98af
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发表时间：
2022-07
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影响因子：
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通讯作者：
A. Dabbech;M. Terris;A. Jackson;M. Ramatsoku;O. Smirnov;Y. Wiaux
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作者：
A. Dabbech;M. Terris;A. Jackson;M. Ramatsoku;O. Smirnov;Y. Wiaux
我们推出了第一个基于人工智能的深度、超分辨率、宽视场射电干涉成像框架，并在 ESO 137-006 射电星系的观测中进行了演示。解决图像重建逆问题的算法框架建立在最近的“即插即用”方案的基础上，其中将去噪算子作为优化算法中的图像正则化器注入，该算法交替进行，直到去噪步骤和梯度下降数据保真度步骤之间收敛。我们研究手工制作和学习的高分辨率、高动态范围降噪器的变体。我们提出了一种并行算法实现，依赖于将图像自动分解为面，并将测量算子自动分解为稀疏低维块，从而实现大数据和图像维度的可扩展性。我们在宽视场验证了我们的图像形成框架，其中包含来自 19 GB MeerKAT 数据（频率为 1053 和 1399 MHz）的 ESO 137-006。恢复的地图显示出比 CLEAN 更高的分辨率和动态范围，揭示了靠近银河系核心的准直同步加速器线。
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 regularizer in an optimization 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 GB 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.