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

Andrew Lizarraga, Eric Hanchen Jiang, Jacob Nowack, Yun Qi Li, Ying Nian Wu +2 more
11/27/2024
astro-ph.GAcs.CV

No official code found. Best implementation: astrodatalab/lizarraga_2024 (5 stars, last commit 2024-12-05).

Abstract

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, Sérsic 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.

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Code Implementations(1)

astrodatalab/lizarraga_2024Community100%

Repository for Learning the Evolution of Physical Structure of Galaxies via Diffusion Models by Lizarraga et al. 2024

51Python, Jupyter NotebookNov 26, 20241 years ago

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Cite this paper

@article{lizarraga2024understanding,
  title  = {Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion Models},
  author = {Andrew Lizarraga and Eric Hanchen Jiang and Jacob Nowack and Yun Qi Li and Ying Nian Wu and Bernie Boscoe and Tuan Do},
  year   = {2024},
  eprint = {2411.18440},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2411.18440v2}
}

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