53 citations · 78 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
Missing Data Imputation for Galaxy Redshift Estimation
Kieran J. Luken, Rabina Padhy, X. Rosalind Wang
Astronomical data is full of holes. While there are many reasons for this missing data, the data can be randomly missing, caused by things like data corruptions or unfavourable obs…
Cataloging the radio-sky with unsupervised machine learning: a new approach for the SKA era
T. J. Galvin, M. Huynh, R. P. Norris +6
We develop a new analysis approach towards identifying related radio components and their corresponding infrared host galaxy based on unsupervised machine learning methods. By expl…