24 citations · 32 across the 5 of their papers we have counts for
5 papers
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…
RG-CAT: Detection Pipeline and Catalogue of Radio Galaxies in the EMU Pilot Survey
Nikhel Gupta, Ray P. Norris, Zeeshan Hayder +15
We present source detection and catalogue construction pipelines to build the first catalogue of radio galaxies from the 270 pilot survey of the Evolutionary Map of the…
Deep Learning for Morphological Identification of Extended Radio Galaxies using Weak Labels
Nikhel Gupta, Zeeshan Hayder, Ray P. Norris +7
The present work discusses the use of a weakly-supervised deep learning algorithm that reduces the cost of labelling pixel-level masks for complex radio galaxies with multiple comp…
Measuring photometric redshifts for high-redshift radio source surveys
Kieran J. Luken, Ray P. Norris, X. Rosalind Wang +3
With the advent of deep, all-sky radio surveys, the need for ancillary data to make the most of the new, high-quality radio data from surveys like the Evolutionary Map of the Unive…
Discovery of Peculiar Radio Morphologies with ASKAP using Unsupervised Machine Learning
Nikhel Gupta, Minh Huynh, Ray P. Norris +5
We present a set of peculiar radio sources detected using an unsupervised machine learning method. We use data from the Australian Square Kilometre Array Pathfinder (ASKAP) telesco…