We develop a new analysis approach towards identifying related radio components and their corresponding infrared host galaxy based on unsupervised machine learning methods. By exploiting PINK, a self-organising map algorithm, we are able to associate radio and infrared sources without the a priori requirement of training labels. We present an example of this method using $894,415$ images from the FIRST and WISE surveys centred towards positions described by the FIRST catalogue. We produce a set of catalogues that complement FIRST and describe 802,646 objects, including their radio components and their corresponding AllWISE infrared host galaxy. Using these data products we (i) demonstrate the ability to identify objects with rare and unique radio morphologies (e.g. 'X'-shaped galaxies, hybrid FR-I/FR-II morphologies), (ii) can identify the potentially resolved radio components that are associated with a single infrared host and (iii) introduce a "curliness" statistic to search for bent and disturbed radio morphologies, and (iv) extract a set of 17 giant radio galaxies between 700-1100 kpc. As we require no training labels, our method can be applied to any radio-continuum survey, provided a sufficiently representative SOM can be trained.
为校准平方公里阵列观测数据而生成的天空模型需要一种快速的自动点源检测和表征方法。近年来，二维图像中的点源检测已经通过使用几种阈值方法来实现。在SKA的第一阶段，我们需要一个能够处理超大图像（80，000 x 80，000像素）的快速实现。虽然底层算法随着图像大小适当地缩放，但是本实施方式不这样。我们提出了一些意见的相关权衡缩放这些实现SKA级别。
The Role of Artificial Intelligence in the SKA Era
Centre for Artificial Intelligence, Zurich University of Applied Sciences ZHAW（苏黎世应用科学大学人工智能中心）；Institute of Business Information Technology, Zurich University of Applied Sciences ZHAW（苏黎世应用科学大学商业信息技术研究所）
Faraday Tomography with the SKA: A New Era of Cosmic Magnetism Studies
Universidad de Santiago de Chile（圣地亚哥智利大学）；University of Manchester（曼彻斯特大学）；Alan Turing Institute（艾伦·图灵研究所）；The University of Calgary（卡尔加里大学）；University of Cape Town（开普敦大学）；National Research Council Canada（加拿大国家研究委员会）；University of Tasmania（塔斯马尼亚大学）
Deep Investigation of Neutral Gas Origins (DINGO): Options for the Processing and Storage of Radio Astronomy Data for robust Deep Spectral Line Imaging in the SKA-Era using uv-Grids
International Centre for Radio Astronomy Research, University of Western Australia（西澳大利亚大学国际无线电天文研究中心）；Australian SKA Regional Centre (AusSRC), University of Western Australia（西澳大利亚大学澳大利亚SKA区域中心）；Pawsey Supercomputing Research Centre（珀西超级计算研究中心）；Oak Ridge National Laboratory（橡树岭国家实验室）；Australia Telescope National Facility, CSIRO Space & Astronomy（CSIRO空间与天文澳大利亚望远镜国家设施）；ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)（ARC三维全天空天体物理学卓越中心）
Accelerating radio astronomy imaging with RICK: a step towards SKA-Mid and SKA-Low
INAF-Osservatorio astronomico di Brera（意大利国家天体物理研究所布雷拉天文台）；ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum Computing（高性能计算、大数据与量子计算国家研究中心）；Dipartimento di Fisica e Astronomia, Università di Bologna（博洛尼亚大学物理与天文学系）；Istituto di Radioastronomia, INAF（意大利国家天体物理研究所射电天文研究所）；INAF-Osservatorio Astronomico di Trieste（意大利国家天体物理研究所的里雅斯特天文台）
RICK 2.0 利用 HeFFTe 库实现高效分布式快速傅里叶变换，提升射电成像性能，适用于 SKA-Mid 和 SKA-Low 项目。
Deep Investigation of Neutral Gas Origins (DINGO): Options for robust Deep Spectral Line Imaging in the SKA-Era
University of Western Australia（西澳大学）；Australia Telescope National Facility, CSIRO Space & Astronomy（澳大利亚望远镜国家设施，CSIRO空间与天文学部）；International Centre for Radio Astronomy Research (ICRAR)（国际射电天文研究中心）；ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)（ARC三维全天空天体物理学卓越中心）；Pawsey Supercomputing Research Centre（Pawsey超级计算研究中心）
DUG Insight: A software package for big-data analysis and visualisation, and its demonstration for passive radar space situational awareness using radio telescopes
