43 citations · 131 across the 8 of their papers we have counts for
5 papers · 1 filter
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,…
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…
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…
Fanaroff-Riley classification of radio galaxies using group-equivariant convolutional neural networks
Anna M. M. Scaife, Fiona Porter
Weight sharing in convolutional neural networks (CNNs) ensures that their feature maps will be translation-equivariant. However, although conventional convolutions are equivariant…