136 citations · 2.6k across the 92 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…
Radio Galaxy Zoo: Unsupervised Clustering of Convolutionally Auto-encoded Radio-astronomical Images
Nicholas O. Ralph, Ray P. Norris, Gu Fang +8
This paper demonstrates a novel and efficient unsupervised clustering method with the combination of a Self-Organising Map (SOM) and a convolutional autoencoder. The rapidly increa…
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…
Testing General Relativity with geodetic VLBI: what profit from a single, specially designed experiment?
Oleg Titov, Anastasiia Girdiuk, Sebastien Lambert +15
Context. We highlight the capabilities of the geodetic VLBI technique to test General relativity in the classical astrometric style, i.e., measuring the deflection of light in the…
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…