# Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks

arXiv: https://arxiv.org/abs/1811.03081 · HTML: https://mlsanity.com/paper/1811.03081/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2018.

## Built on by (most cited first, 8 of 8)

- [Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy](https://mlsanity.com/paper/2211.03796/) (2022)
- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [The CAMELS project: public data release](https://mlsanity.com/paper/2201.01300/) (2022)
- [Diffusion Models for Probabilistic Deconvolution of Galaxy Images](https://mlsanity.com/paper/2307.11122/) (2023)
- [Generative Adversarial Networks for Astronomical Images Generation](https://mlsanity.com/paper/2111.11578/) (2021)
- [Category-based Galaxy Image Generation via Diffusion Models](https://mlsanity.com/paper/2506.16255/) (2025)
- [Galaxy Image Translation with Semi-supervised Noise-reconstructed Generative Adversarial Networks](https://mlsanity.com/paper/2101.07389/) (2021)
- [Amplifying the imaging power of digital sky surveys with space telescopes data and generative AI](https://mlsanity.com/paper/2608.20666/) (2026)

## Abstract (arXiv)

Astronomy of the 21st century increasingly finds itself with extreme quantities of data. This growth in data is ripe for modern technologies such as deep image processing, which has the potential to allow astronomers to automatically identify, classify, segment and deblend various astronomical objects. In this paper, we explore the use of chained generative adversarial networks (GANs), a class of generative models that learn mappings from latent spaces to data distributions by modelling the joint distribution of the data, to produce physically realistic galaxy images as one use case of such models. In cosmology, such datasets can aid in the calibration of shape measurements for weak lensing by augmenting data with synthetic images. By measuring the distributions of multiple physical properties, we show that images generated with our approach closely follow the distributions of real galaxies, further establishing state-of-the-art GAN architectures as a valuable tool for modern-day astronomy.