Extracting Meaningful Features from Early-Science Radio Data

Matthew Alger1,2, Prof. Naomi McClure-Griffiths1, Dr Cheng Soon Ong3

1Research School of Astronomy and Astrophysics, The Australian National University, Canberra, Australia, 2Data61, CSIRO, Acton, Australia, 3Research School of Computer Science, The Australian National University, Acton, Australia


Early-science data from the Australian Square Kilometre Array Pathfinder (ASKAP) are coming fast. Wide-area radio projects such as the Evolutionary Map of the Universe (EMU) and the Polarisation Sky Survey of the Universe’s Magnetism (POSSUM) already have terabytes of ASKAP observations for use in early-science and survey planning. We have applied unsupervised machine learning methods to learn a meaningful representation of these early-science observations and demonstrated that this representation generalises across different sets of observations. We use this representation to address physical problems such as polarised source characterisation and physical model-fitting. Our approach provides a way to use early-science data even without full understanding of the unique instrumentation effects brought to the table by ASKAP.


Matthew is an astrophysics student at the Australian National University and Data61. They are interested in applications of machine learning to wide-area radio surveys, with a focus on the upcoming EMU (continuum) and POSSUM (polarisation) surveys to be conducted with the Australian Square Kilometre Array Pathfinder.


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