Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation

TL;DR

This paper introduces GalaxySD, a conditional diffusion model that synthesizes realistic galaxy images to augment training data, significantly improving galaxy morphology classification and rare object detection in astronomical surveys.

Contribution

The paper presents a novel generative model, GalaxySD, that produces high-fidelity galaxy images conditioned on morphology, enhancing machine learning performance and enabling extrapolation to unseen domains.

Findings

Improved classification metrics by up to 30% with augmented data.

Doubled detection of rare galaxy objects from 352 to 872.

Generated images closely match specified morphological features.

Abstract

Observational astronomy relies on visual feature identification to detect critical astrophysical phenomena. While machine learning (ML) increasingly automates this process, models often struggle with generalization in large-scale surveys due to the limited representativeness of labeled datasets, whether from simulations or human annotation, a challenge pronounced for rare yet scientifically valuable objects. To address this, we propose a conditional diffusion model to synthesize realistic galaxy images for augmenting ML training data (hereafter GalaxySD). Leveraging the Galaxy Zoo 2 dataset which contains visual feature, galaxy image pairs from volunteer annotation, we demonstrate that GalaxySD generates diverse, high-fidelity galaxy images that closely adhere to the specified morphological feature conditions. Moreover, this model enables generative extrapolation to project well-annotated data into unseen domains and advancing rare object detection. Integrating synthesized images into ML pipelines improves performance in standard morphology classification, boosting completeness and purity by up to 30% across key metrics. For rare object detection, using early-type galaxies with prominent dust lane features (~0.1% in GZ2 dataset) as a test case, our approach doubled the number of detected instances, from 352 to 872, compared to previous studies based on visual inspection. This study highlights the power of generative models to bridge gaps between scarce labeled data and the vast, uncharted parameter space of observational astronomy and sheds insight for future astrophysical foundation model developments. Our project homepage is available at https://galaxysd-webpage.streamlit.app/.