# Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion Models

arXiv: https://arxiv.org/abs/2411.18440 · HTML: https://mlsanity.com/paper/2411.18440/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models.
- Builds on 19 of its 43 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Placed in

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

## Builds on (most cited first, 19 of 19)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Taming Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2012.09841/) (2020)
- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)
- [Analyzing and Improving the Training Dynamics of Diffusion Models](https://mlsanity.com/paper/2312.02696/) (2023)
- [Realistic galaxy image simulation via score-based generative models](https://mlsanity.com/paper/2111.01713/) (2021)
- [Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inference](https://mlsanity.com/paper/2402.04647/) (2024)
- [Think Twice Before You Act: Improving Inverse Problem Solving With MCMC](https://mlsanity.com/paper/2409.08551/) (2024)
- [How DREAMS are made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Clouds](https://mlsanity.com/paper/2409.02980/) (2024)
- [Diffusion Models for Probabilistic Deconvolution of Galaxy Images](https://mlsanity.com/paper/2307.11122/) (2023)
- [Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theory](https://mlsanity.com/paper/2411.17472/) (2024)
- [Alignment of Tractography Streamlines using Deformation Transfer via Parallel Transport](https://mlsanity.com/paper/2108.03697/) (2021)
- [StreamNet: A WAE for White Matter Streamline Analysis](https://mlsanity.com/paper/2209.01498/) (2022)
- [Using Galaxy Evolution as Source of Physics-Based Ground Truth for Generative Models](https://mlsanity.com/paper/2407.07229/) (2024)
- [Differentiable VQ-VAE's for Robust White Matter Streamline Encodings](https://mlsanity.com/paper/2311.06212/) (2023)

## Built on by (most cited first, 4 of 4)

- [Multi-Modal Masked Autoencoders for Learning Image-Spectrum Associations for Galaxy Evolution and Cosmology](https://mlsanity.com/paper/2510.22527/) (2025)
- [Combining datasets with different ground truths using Low-Rank Adaptation to generalize image-based CNN models for photometric redshift prediction](https://mlsanity.com/paper/2601.00146/) (2026)
- [Accelerating Redshift-Conditioned Galaxy Image Synthesis with One-step Generative Modeling](https://mlsanity.com/paper/2605.17546/) (2026)
- [AquiLLM: Evaluating Faithfulness in Open-Weight RAG-LLM Systems for Scientific Research](https://mlsanity.com/paper/2609.16519/) (2026)

## Abstract (arXiv)

Redshift measures the distance to galaxies and underlies our understanding of the origin of the Universe and galaxy evolution. Spectroscopic redshift is the gold-standard method for measuring redshift, but it requires about 1000 times more telescope time than broad-band imaging. That extra cost limits sky coverage and sample size and puts large spectroscopic surveys out of reach. Photometric redshift methods rely on imaging in multiple color filters and template fitting, yet they ignore the wealth of information carried by galaxy shape and structure. We demonstrate that a diffusion model conditioned on continuous redshift learns this missing joint structure, reproduces known morphology-z correlations. We verify on the HyperSuprime-Cam survey, that the model captures redshift-dependent trends in ellipticity, semi-major axis, S'ersic index, and isophotal area that these generated images correlate closely with true redshifts on test data. To our knowledge this is the first study to establish a direct link between galaxy morphology and redshift. Our approach offers a simple and effective path to redshift estimation from imaging data and will help unlock the full potential of upcoming wide-field surveys.