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

arXiv: https://arxiv.org/abs/2506.16233 · HTML: https://mlsanity.com/paper/2506.16233/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 13 of its 121 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Placed in

- Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/)

## Builds on (most cited first, 13 of 13)

- [Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws](https://mlsanity.com/paper/2303.03192/) (2023)
- [Realistic galaxy image simulation via score-based generative models](https://mlsanity.com/paper/2111.01713/) (2021)
- [Self-Supervised Representation Learning for Astronomical Images](https://mlsanity.com/paper/2012.13083/) (2020)
- [Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data](https://mlsanity.com/paper/1906.11248/) (2019)
- [deepCR: Cosmic Ray Rejection with Deep Learning](https://mlsanity.com/paper/1907.09500/) (2019)
- [Mining for Strong Gravitational Lenses with Self-supervised Learning](https://mlsanity.com/paper/2110.00023/) (2021)
- [Image reconstruction algorithms in radio interferometry: from handcrafted to learned regularization denoisers](https://mlsanity.com/paper/2202.12959/) (2022)
- [Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift](https://mlsanity.com/paper/2204.08816/) (2022)
- [Star-galaxy Classification Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1608.04369/) (2016)
- [First AI for deep super-resolution wide-field imaging in radio astronomy: unveiling structure in ESO 137--006](https://mlsanity.com/paper/2207.11336/) (2022)
- [Fink: early supernovae Ia classification using active learning](https://mlsanity.com/paper/2111.11438/) (2021)
- [Cycle-StarNet: Bridging the gap between theory and data by leveraging large datasets](https://mlsanity.com/paper/2007.03109/) (2020)
- [Cosmic-CoNN: A Cosmic Ray Detection Deep-Learning Framework, Dataset, and Toolkit](https://mlsanity.com/paper/2106.14922/) (2021)

## Abstract (arXiv)

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/.