mlsanityData to 14 Oct 2026

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

arXiv 2411.18440 ↗First posted 27 Nov 2024astro-ph.GA cs.CVcited by 4

On the map: Diffusion models → Inverse problems and image restoration

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.

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.

Builds on

19 of 43 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Think Twice Before You Act: Improving Inverse Problem Solving With MCMC13 Sep
  2. 2024Using Galaxy Evolution as Source of Physics-Based Ground Truth for Generative Models9 Jul
  3. 2023Diffusion Models for Probabilistic Deconvolution of Galaxy Images20 Jul
  4. 2021Realistic galaxy image simulation via score-based generative models2 Nov

Other references · 15

  1. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  2. 2020Denoising Diffusion Implicit Modelscited by 9,781
  3. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  6. 2016Gaussian Error Linear Units (GELUs)cited by 3,502
  7. 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
  8. 2017Wasserstein GANcited by 639
  9. 2023Analyzing and Improving the Training Dynamics of Diffusion Modelscited by 402
  10. 2024Latent Plan Transformer for Trajectory Abstraction: Planning as Latent Space Inferencecited by 14
  11. 2024How DREAMS are made: Emulating Satellite Galaxy and Subhalo Populations with Diffusion Models and Point Cloudscited by 5
  12. 2024Unlocking the Potential of Text-to-Image Diffusion with PAC-Bayesian Theorycited by 3
3 more
  1. 2021Alignment of Tractography Streamlines using Deformation Transfer via Parallel Transportcited by 2
  2. 2022StreamNet: A WAE for White Matter Streamline Analysiscited by 2
  3. 2023Differentiable VQ-VAE's for Robust White Matter Streamline Encodingscited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Multi-Modal Masked Autoencoders for Learning Image-Spectrum Associations for Galaxy Evolution and Cosmologycited by 2
  2. 2026Combining datasets with different ground truths using Low-Rank Adaptation to generalize image-based CNN models for photometric redshift predictioncited by 1
  3. 2026Accelerating Redshift-Conditioned Galaxy Image Synthesis with One-step Generative Modelingcited by 0
  4. 2026AquiLLM: Evaluating Faithfulness in Open-Weight RAG-LLM Systems for Scientific Researchcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Investigation on deep learning-based galaxy image translation models
  2. 2026Generative Diffusion Priors for 3D Mapping of the Dark Universe
  3. 2025Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation
  4. 2024Galaxy spectroscopy without spectra: Galaxy properties from photometric images with conditional diffusion models
  5. 2025Conditional Diffusion-Flow models for generating 3D cosmic density fields: applications to f(R) cosmologies
  6. 2023Unified framework for diffusion generative models in SO(3): applications in computer vision and astrophysics
  7. 2024Bayesian Deconvolution of Astronomical Images with Diffusion Models: Quantifying Prior-Driven Features in Reconstructions
  8. 2026Cosmo-FOLD: Fast generation and upscaling of field-level cosmological maps with overlap latent diffusion