97 citations · 548 across the 18 of their papers we have counts for
5 papers · 1 filter
Rotation and flipping invariant self-organizing maps with astronomical images: A cookbook and application to the VLA Sky Survey QuickLook images
A. N. Vantyghem, T. J. Galvin, B. Sebastian +12
Modern wide field radio surveys typically detect millions of objects. Techniques based on machine learning are proving to be useful for classifying large numbers of objects. The se…
RadioGalaxyNET: Dataset and Novel Computer Vision Algorithms for the Detection of Extended Radio Galaxies and Infrared Hosts
Nikhel Gupta, Zeeshan Hayder, Ray P. Norris +2
Creating radio galaxy catalogues from next-generation deep surveys requires automated identification of associated components of extended sources and their corresponding infrared h…
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…
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…