14 citations · 32 across the 8 of their papers we have counts for
8 papers
Radio Galaxy Zoo: Morphological classification by Fanaroff-Riley designation using self-supervised pre-training
Nutthawara Buatthaisong, Inigo Val Slijepcevic, Anna M. M. Scaife +5
In this study, we examine over 14,000 radio galaxies finely selected from Radio Galaxy Zoo (RGZ) project and provide classifications for approximately 5,900 FRIs and 8,100 FRIIs. W…
Radio Galaxy Zoo: Towards building the first multi-purpose foundation model for radio astronomy with self-supervised learning
Inigo V. Slijepcevic, Anna M. M. Scaife, Mike Walmsley +4
In this work, we apply self-supervised learning with instance differentiation to learn a robust, multi-purpose representation for image analysis of resolved extragalactic continuum…
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…
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…
Can semi-supervised learning reduce the amount of manual labelling required for effective radio galaxy morphology classification?
Inigo V. Slijepcevic, Anna M. M. Scaife
In this work, we examine the robustness of state-of-the-art semi-supervised learning (SSL) algorithms when applied to morphological classification in modern radio astronomy. We tes…