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

2024-11-27

AI Summary

Spectroscopic redshift measurements are costly, while photometric methods neglect morphological information. To address this, we propose an end-to-end redshift estimation framework based on a conditional diffusion model. The model conditions directly on continuous redshift values and learns the joint distribution of galaxy morphology and redshift from imaging data—establishing, for the first time, a template-free, redshift-sensitive image generation and inference paradigm. We incorporate morphological parameters—including ellipticity and Sérsic index—for statistical validation and train the model on Hyper Suprime-Cam (HSC) survey data. Experiments demonstrate that generated images accurately reproduce redshift-dependent morphological evolution trends, and redshift estimation on the test set achieves significantly improved correlation with ground-truth spectroscopic redshifts. This work introduces a computationally efficient, morphology-aware redshift estimation paradigm tailored for wide-field imaging surveys.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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, 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.

Problem

• Estimating galaxy redshifts using imaging data efficiently
• Linking galaxy morphology to redshift via diffusion models
• Improving redshift accuracy by incorporating structural galaxy features

Innovation

• Redshift conditioned diffusion models learn joint structure
• Model captures redshift-dependent galaxy morphology trends
• Direct link between galaxy morphology and redshift