# Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning

## TL;DR

This paper introduces Bayesian CNNs combined with active learning to improve galaxy morphology classification, reducing the need for labeled data and enabling scalable, reliable analysis of large galaxy surveys.

## Contribution

It presents a novel Bayesian CNN approach with a generative model for volunteer responses and an active learning strategy, significantly reducing labeling effort.

## Key findings

- Posteriors are well-calibrated and reliable.
- Active learning reduces labeling needs by up to 60%.
- Method enables scalable galaxy morphology classification.

## Abstract

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. Further, using our posteriors, we apply the active learning strategy BALD to request volunteer responses for the subset of galaxies which, if labelled, would be most informative for training our network. We show that training our Bayesian CNNs using active learning requires up to 35-60% fewer labelled galaxies, depending on the morphological feature being classified. By combining human and machine intelligence, Galaxy Zoo will be able to classify surveys of any conceivable scale on a timescale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution.