42 citations · 110 across the 17 of their papers we have counts for
6 papers · 1 filter
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…
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…
A Compressed Sensing Faraday Depth Reconstruction Framework for the MeerKAT MIGHTEE-POL Survey
Miguel Cárcamo, Anna Scaife, Russ Taylor +5
In this work we present a novel compute framework for reconstructing Faraday depth signals from noisy and incomplete spectro-polarimetric radio datasets. This framework is based on…
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…