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Radio astronomy faces a data deluge from new radio telescopes and wide-area surveys in the lead-up to the Square Kilometre Array. The hope is that machine learning, a computer science field that designs methods for extracting useful information from data, will provide algorithms to help process this huge amount of data. In this talk I summarise my work on applying machine learning to extragalactic sources, including the first machine learning algorithm for radio cross-identification and an innovative new method for characterising the Faraday complexity of polarised sources. |
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