论文标题
用无监督的机器学习将无线电天线分类：SKA时代的新方法
Cataloging the radio-sky with unsupervised machine learning: a new approach for the SKA era
论文作者
论文摘要
我们开发了一种基于无监督的机器学习方法来识别相关无线电组件及其相应的红外宿主星系的新分析方法。通过利用粉红色（一种自组织图算法），我们可以将无线电和红外线来源关联，而无需先验培训标签。我们使用$ 894,415 $的图像从第一目录中描述的位置中为中心进行了$ 894,415 $的图像提供了一个示例。我们生产一组首先补充的目录，并描述802,646个对象，包括它们的无线电组件及其相应的共同红外宿主星系。使用这些数据产品，我们（i）我们证明了具有罕见和独特无线形态（例如'X'形星系，混合FR-I/FR-II形态）的物体的能力，（ii）可以识别与单一的红外宿主和（iii）引入“ curliness”和“ curlistiv”的“稳定性”式的“ cerlive and toctiv”的无线电，以获取“稳定性”和“ curl”的搜索。 17个巨大的射电星系在700-1100 kpc之间。由于我们不需要培训标签，只要可以训练足够的代表性SOM，我们的方法就可以应用于任何无线电调查。
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.
