Publications (9)
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…
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…
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…
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…
Weight Pruning and Uncertainty in Radio Galaxy Classification
Devina Mohan, Anna Scaife
In this work we use variational inference to quantify the degree of epistemic uncertainty in model predictions of radio galaxy classification and show that the level of model poste…
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…
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…
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…
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…