201 citations · 647 across the 31 of their papers we have counts for
7 papers · 1 filter
Galaxy And Mass Assembly: Galaxy Morphology in the Green Valley, Prominent rings and looser Spiral Arms
Dominic Smith, Lutz Haberzettl, L. E. Porter +15
Galaxies broadly fall into two categories: star-forming (blue) galaxies and quiescent (red) galaxies. In between, one finds the less populated ``green valley". Some of these galaxi…
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…
Galaxy Zoo: Clump Scout -- Design and first application of a two-dimensional aggregation tool for citizen science
Hugh Dickinson, Dominic Adams, Vihang Mehta +9
Galaxy Zoo: Clump Scout is a web-based citizen science project designed to identify and spatially locate giant star forming clumps in galaxies that were imaged by the Sloan Digital…
Learning useful representations for radio astronomy "in the wild" with contrastive learning
Inigo Val Slijepcevic, Anna M. M. Scaife, Mike Walmsley +1
Unknown class distributions in unlabelled astrophysical training data have previously been shown to detrimentally affect model performance due to dataset shift between training and…
Towards Galaxy Foundation Models with Hybrid Contrastive Learning
Mike Walmsley, Inigo Val Slijepcevic, Micah Bowles +1
New astronomical tasks are often related to earlier tasks for which labels have already been collected. We adapt the contrastive framework BYOL to leverage those labels as a pretra…
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…
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…