Radio Galaxy Zoo: citizen science, machine learning, and serendipity
by Julie Banfield (ANU)
Abstract.
In this presentation I will provide the science results from the first
Radio Galaxy Zoo data release. I will present findings on our
serendipitous discoveries of one of the largest known radio sources,
spiral galaxies hosting radio-loud AGN, and the large population of
galaxies showing evidence of jet-induced star formation (positive
feedback) called green DRAGNs. I will also highlight the Radio Galaxy
Zoo results from previously unreported giant radio galaxies and hybrid
morphology radio sources. In order to prepare for the large data
volumes that will be available in the next few years, we have begun
machine learning collaborations to tackle the issue of radio source
identification with their corresponding host galaxy. The results of
our machine learning work will also be presented and will highlight
the state of this research area for the upcoming pre-SKA era.
