4 papers
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…
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…