43 citations · 131 across the 8 of their papers we have counts for
8 papers
Combining astrophysical datasets with CRUMB
Fiona A. M. Porter, Anna M. M. Scaife
At present, the field of astronomical machine learning lacks widely-used benchmarking datasets; most research employs custom-made datasets which are often not publicly released, ma…
MiraBest: A Dataset of Morphologically Classified Radio Galaxies for Machine Learning
Fiona A. M. Porter, Anna M. M. Scaife
The volume of data from current and future observatories has motivated the increased development and application of automated machine learning methodologies for astronomy. However,…
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…
A New Task: Deriving Semantic Class Targets for the Physical Sciences
Micah Bowles, Hongming Tang, Eleni Vardoulaki +8
We define deriving semantic class targets as a novel multi-modal task. By doing so, we aim to improve classification schemes in the physical sciences which can be severely abstract…
Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification
Devina Mohan, Anna M. M. Scaife, Fiona Porter +2
In this work we use variational inference to quantify the degree of uncertainty in deep learning model predictions of radio galaxy classification. We show that the level of model p…
Structured Variational Inference for Simulating Populations of Radio Galaxies
David J. Bastien, Anna M. M. Scaife, Hongming Tang +2
We present a model for generating postage stamp images of synthetic Fanaroff-Riley Class I and Class II radio galaxies suitable for use in simulations of future radio surveys such…