102 citations · 288 across the 10 of their papers we have counts for
9 papers
Detecting HI Self-Absorption using Neural Networks
Eric G. M. Muller, Naomi M. McClure-Griffiths, Hiep Nguyen +5
Cold atomic hydrogen plays a crucial role in the life cycle of interstellar gas. It serves as the intermediary phase in the condensation and cooling processes that bridge the warm…
Radio Galaxy Zoo Data Release 1: 100,185 radio source classifications from the FIRST and ATLAS surveys
O. Ivy Wong, A. F. Garon, M. J. Alger +15
Radio galaxies can extend far beyond the stellar component of their originating host galaxies, and their radio emission can consist of multiple discrete components. Furthermore, th…
TPCNet: Representation learning for HI mapping
Hiep Nguyen, Haiyang Tang, Matthew Alger +4
We introduce TPCNet, a neural network predictor that combines Convolutional and Transformer architectures with Positional encodings, for neutral atomic hydrogen (HI) spectral analy…
Radio Galaxy Zoo: tagging radio subjects using text
Dawei Chen, Vinay Kerai, Matthew J. Alger +2
RadioTalk is a communication platform that enabled members of the Radio Galaxy Zoo (RGZ) citizen science project to engage in discussion threads and provide further descriptions of…
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…
Heightened Faraday Complexity in the inner 1 kpc of the Galactic Centre
J. D. Livingston, N. M. McClure-Griffiths, B. M. Gaensler +2
We have measured the Faraday rotation of 62 extra-galactic background sources in 58 fields using the CSIRO Australia Telescope Compact Array (ATCA) with a frequency range of 1.1 -…
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: 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…