Lectures_Thursday_September 06 2018_Rafael_Mostert

Rafaël Mostert: " Unveiling the morphologies of the the LOFAR Two-metre Sky Survey radio source population with self-organised maps " The Low Frequency Array (LOFAR) Two-metre Sky Survey (LoTSS) is undertaking a low-frequency radio continuum survey of the sky at unparalleled resolution and sensitivity. In order to fully exploit this huge dataset and the tens of Tbit/s produced by the SKA in the next decade, automated methods in machine learning and data- ining are going to become increasingly essential both for identifying optical counterparts to the radio sources and for morphological classifications. Using Self- rganizing Maps (SOMs), a form of unsupervised machine learning, we explore the diversity of radio morphologies for the ~20k extended radio continuum sources in the LoTSS first data release (a few percent of the final LoTSS survey). We make use of PINK (Polsterer et al.), a code which extends the SOM algorithm with rotation and flipping invariance, increasing its suitability and effectiveness for training on astronomical sources. After training, the SOMs can be used for a wide range of science exploitation and we present an illustration of their potential for mining the full LoTSS survey: finding an arbitrary number of the morphologically most rare sources in our data. Objects found this way span a wide range of morphological and physical categories: extended jets of radio AGN, diffuse cluster haloes/relics and nearby spiral alaxies. Finally, to enable accessible, interactive and intuitive data exploration we showcase the LOFAR-PINK Visualization Tool that allows users to easily explore the LoTSS dataset through the trained SOMs.