2 citations · 3 across the 11 of their papers we have counts for
11 papers
Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group
Yuan-Sen Ting, Digvijay Wadekar, Phill Cargile +21
Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely availab…
Monte Carlo conformal prediction for quantifying uncertainty in radio galaxy classification under ambiguous ground truth
Alex Walls, James Barry, Devina Mohan +1
Dramatically increasing data volumes are forcing astronomers to adopt automated methods for the identification and classification of astronomical objects. Although deep-learning mo…
Variational views for self-supervised learning in radio astronomy
Johnny Joseph Alphonse, Anna M. M. Scaife
Modern astronomical surveys are producing progressively larger and more complex datasets, making traditional supervised approaches that rely on extensive labelled catalogues increa…
Galaxy Zoo Evo: 1 million human-annotated images of galaxies
Mike Walmsley, Steven Bamford, Hugh Dickinson +17
We introduce Galaxy Zoo Evo, a labeled dataset for building and evaluating foundation models on images of galaxies. GZ Evo includes 104M crowdsourced labels for 823k images from fo…
Natural gradient descent for improving variational inference based classification of radio galaxies
Devina Mohan, Anna M. M. Scaife
Bayesian neural networks (BNNs) are most commonly optimised with first-order optimisers such as stochastic gradient descent. However, when optimising for parameters of probabilisti…
Intrinsic Dimension Estimation for Radio Galaxy Zoo using Diffusion Models
Joan Font-Quer Roset, Devina Mohan, Anna Scaife
In this work, we estimate the intrinsic dimension (iD) of the Radio Galaxy Zoo (RGZ) dataset using a score-based diffusion model. We examine how the iD estimates vary as a function…
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…
Evaluating Bayesian deep learning for radio galaxy classification
Devina Mohan, Anna M. M. Scaife
The radio astronomy community is rapidly adopting deep learning techniques to deal with the huge data volumes expected from the next generation of radio observatories. Bayesian neu…
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…
MCMC to address model misspecification in Deep Learning classification of Radio Galaxies
Devina Mohan, Anna Scaife
The radio astronomy community is adopting deep learning techniques to deal with the huge data volumes expected from the next-generation of radio observatories. Bayesian neural netw…