Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning

Understand

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies.

• Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label.
• Our posteriors are well-calibrated (e.g.
• for predicting bars, we achieve coverage errors of 11.8% within a vote fraction deviation of 0.2) and hence are reliable for practical use.