42 citations · 110 across the 17 of their papers we have counts for
18 papers
Radio Galaxy Zoo EMU: Harnessing Citizen Science and AI to Advance Open Science Catalogues
Eleni Vardoulaki, Hongming Tang, Micah Bowles +10
Over the past decades, significant efforts have been devoted to developing sophisticated algorithms for automatically identifying and classifying radio sources in large surveys. Ho…
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…
IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors
Noé Dia, M. J. Yantovski-Barth, Alexandre Adam +4
Inferring sky surface brightness distributions from noisy interferometric data in a principled statistical framework has been a key challenge in radio astronomy. In this work, we i…
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
The Multimodal Universe Collaboration, Jeroen Audenaert, Micah Bowles +26
We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MU…
Scaling Laws for Galaxy Images
Mike Walmsley, Micah Bowles, Anna M. M. Scaife +17
We present the first systematic investigation of supervised scaling laws outside of an ImageNet-like context - on images of galaxies. We use 840k galaxy images and over 100M annota…
Bayesian Imaging for Radio Interferometry with Score-Based Priors
Noe Dia, M. J. Yantovski-Barth, Alexandre Adam +5
The inverse imaging task in radio interferometry is a key limiting factor to retrieving Bayesian uncertainties in radio astronomy in a computationally effective manner. We use a sc…
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…
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…
E(2) Equivariant Self-Attention for Radio Astronomy
Micah Bowles, Matthew Bromley, Max Allen +1
In this work we introduce group-equivariant self-attention models to address the problem of explainable radio galaxy classification in astronomy. We evaluate various orders of both…
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…
Attention-gating for improved radio galaxy classification
Micah Bowles, Anna M. M. Scaife, Fiona Porter +2
In this work we introduce attention as a state of the art mechanism for classification of radio galaxies using convolutional neural networks. We present an attention-based model th…
Interactions among intermediate redshift galaxies. The case of SDSSJ134420.86+663717.8
Persis Misquitta, Micah Bowles, Andreas Eckart +4
We present the properties of the central supermassive black holes and the host galaxies of the interacting object SDSSJ134420.86+663717.8. We obtained optical long slit spectroscop…