713 citations · 3.9k across the 97 of their papers we have counts for
9 papers · 1 filter
Radio Galaxy Zoo: Towards building the first multi-purpose foundation model for radio astronomy with self-supervised learning
Inigo V. Slijepcevic, Anna M. M. Scaife, Mike Walmsley +4
In this work, we apply self-supervised learning with instance differentiation to learn a robust, multi-purpose representation for image analysis of resolved extragalactic continuum…
Radio Galaxy Zoo: Giant Radio Galaxy Classification using Multi-Domain Deep Learning
H. Tang, A. M. M. Scaife, O. I. Wong +1
In this work, we explore the potential of multi-domain multi-branch convolutional neural networks (CNNs) for identifying comparatively rare giant radio galaxies from large volumes…
Interpretable Faraday Complexity Classification
M. J. Alger, J. D. Livingston, N. M. McClure-Griffiths +3
Faraday complexity describes whether a spectropolarimetric observation has simple or complex magnetic structure. Quickly determining the Faraday complexity of a spectropolarimetric…
Square Kilometre Array Science Data Challenge 1: analysis and results
A. Bonaldi, T. An, M. Bruggen +42
As the largest radio telescope in the world, the Square Kilometre Array (SKA) will lead the next generation of radio astronomy. The feats of engineering required to construct the t…
Science with the Murchison Widefield Array: Phase I Results and Phase II Opportunities
A. P. Beardsley, M. Johnston-Hollitt, C. M. Trott +56
The Murchison Widefield Array (MWA) is an open access telescope dedicated to studying the low frequency (80300 MHz) southern sky. Since beginning operations in mid 2013, the MWA…
Radio Galaxy Zoo: Knowledge Transfer Using Rotationally Invariant Self-Organising Maps
T. J. Galvin, M. Huynh, R. P. Norris +7
With the advent of large scale surveys the manual analysis and classification of individual radio source morphologies is rendered impossible as existing approaches do not scale. Th…
Radio Galaxy Zoo: Machine learning for radio source host galaxy cross-identification
M. J. Alger, J. K. Banfield, C. S. Ong +6
We consider the problem of determining the host galaxies of radio sources by cross-identification. This has traditionally been done manually, which will be intractable for wide-are…
Radio Galaxy Zoo: ClaRAN - A Deep Learning Classifier for Radio Morphologies
Chen Wu, O. Ivy Wong, Lawrence Rudnick +13
The upcoming next-generation large area radio continuum surveys can expect tens of millions of radio sources, rendering the traditional method for radio morphology classification t…
Radio Galaxy Zoo: Compact and extended radio source classification with deep learning
V. Lukic, M. Brüggen, J. K. Banfield +4
Machine learning techniques have been increasingly useful in astronomical applications over the last few years, for example in the morphological classification of galaxies. Convolu…