42 citations · 110 across the 17 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…
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…