49 citations · 83 across the 10 of their papers we have counts for
11 papers
Radio Galaxy Zoo EMU: Harnessing Citizen Science and AI to Advance Open Science Catalogues
Eleni Vardoulaki, Hongming Tang, Micah Bowles +10
Over the past decades, significant efforts have been devoted to developing sophisticated algorithms for automatically identifying and classifying radio sources in large surveys. Ho…
Radio Galaxy Zoo: EMU -- paving the way for EMU cataloging using AI and citizen science
Hongming Tang, Eleni Vardoulaki, RGZ EMU collaboration
The Evolutionary Map of the Universe (EMU) survey with ASKAP is transforming our understanding of radio galaxies, AGN duty cycles, and cosmic structure. EMUCAT efficiently identifi…
Category-based Galaxy Image Generation via Diffusion Models
Xingzhong Fan, Hongming Tang, Yue Zeng +2
Conventional galaxy generation methods rely on semi-analytical models and hydrodynamic simulations, which are highly dependent on physical assumptions and parameter tuning. In cont…
Radio Galaxy Zoo: Leveraging latent space representations from variational autoencoder
Sambatra Andrianomena, Hongming Tang
We propose to learn latent space representations of radio galaxies, and train a very deep variational autoencoder (\protect\Verb+VDVAE+) on RGZ DR1, an unlabeled dataset, to this e…
Galaxy stellar and total mass estimation using machine learning
Jiani Chu, Hongming Tang, Dandan Xu +2
Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning, which reduces the reliance on such assumptions, can be used to determi…
The High-Redshift Clusters Occupied by Bent Radio AGN (COBRA) Survey: Investigating the Role of Environment on Bent Radio AGNs using LOFAR
Emmet Golden-Marx, Emily Moravec, Lu Shen +10
Bent radio AGN morphology depends on the density of the surrounding gas. However, bent sources are found inside and outside clusters, raising the question of how environment impact…
A model local interpretation routine for deep learning based radio galaxy classification
Hongming Tang, Shiyu Yue, Zijun Wang +5
Radio galaxy morphological classification is one of the critical steps when producing source catalogues for large-scale radio continuum surveys. While many recent studies attempted…
Radio Galaxy Zoo EMU: Towards a Semantic Radio Galaxy Morphology Taxonomy
Micah Bowles, Hongming Tang, Eleni Vardoulaki +20
We present a novel natural language processing (NLP) approach to deriving plain English descriptors for science cases otherwise restricted by obfuscating technical terminology. We…
Identifying anomalous radio sources in the EMU Pilot Survey using a complexity-based approach
Gary Segal, David Parkinson, Ray Norris +12
The Evolutionary Map of the Universe (EMU) large-area radio continuum survey will detect tens of millions of radio galaxies, giving an opportunity for the detection of previously u…
Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift
Inigo V. Slijepcevic, Anna M. M. Scaife, Mike Walmsley +4
In this work we examine the classification accuracy and robustness of a state-of-the-art semi-supervised learning (SSL) algorithm applied to the morphological classification of rad…
Radio Galaxy Zoo: The Distortion of Radio Galaxies by Galaxy Clusters
Avery F. Garon, Lawrence Rudnick, O. Ivy Wong +9
We study the impact of cluster environment on the morphology of a sample of 4304 extended radio galaxies from Radio Galaxy Zoo. A total of 87% of the sample lies within a projected…