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We develop a new analysis approach towards identifying related radiocomponents and their corresponding infrared host galaxy based on unsupervisedmachine learning methods. By exploiting PINK, a self-organising map algorithm,we are able to associate radio and infrared sources without the a priorirequirement of training labels. We present an example of this method using$894,415$ images from the FIRST and WISE surveys centred towards positionsdescribed by the FIRST catalogue. We produce a set of catalogues thatcomplement FIRST and describe 802,646 objects, including their radio componentsand their corresponding AllWISE infrared host galaxy. Using these data productswe (i) demonstrate the ability to identify objects with rare and unique radiomorphologies (e.g. 'X'-shaped galaxies, hybrid FR-I/FR-II morphologies), (ii)can identify the potentially resolved radio components that are associated witha single infrared host and (iii) introduce a "curliness" statistic to searchfor bent and disturbed radio morphologies, and (iv) extract a set of 17 giantradio galaxies between 700-1100 kpc. As we require no training labels, ourmethod can be applied to any radio-continuum survey, provided a sufficientlyrepresentative SOM can be trained.

Cataloging the radio-sky with unsupervised machine learning: a new approach for the SKA era