A scalable Bayesian framework for galaxy emission line detection and redshift estimation

2026-03-25

AI Summary

This work proposes the first scalable Bayesian framework that jointly models galaxy redshift estimation and emission-line detection, addressing a key limitation in large-scale spectroscopic surveys where the presence of emission lines is unknown a priori. Traditional approaches struggle to efficiently process massive multimodal datasets under this uncertainty. By leveraging multimodal posterior modeling and parallelized inference, the method achieves both high-precision redshift estimates and reliable emission-line identification while maintaining computational efficiency. The framework has been successfully applied to millions of spectra and is readily adaptable to data from next-generation telescopes such as JWST, Euclid, and Roman, significantly enhancing the practicality and scalability of large-volume spectral analysis.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Estimating galaxy redshifts is crucial for constraining key physical quantities like those in the equation of state of dark energy. Modern telescopes such as the James Webb Space Telescope, the Euclid Space Telescope, and the NASA Nancy Grace Roman Space Telescope are producing massive amounts of spectroscopic data that enable precise redshift estimation. However, a galaxy's redshift can be estimated only when emission lines are present in the observed spectrum, which is unknown a priori. A novel Bayesian approach to estimating redshift and simultaneously testing for the presence of emission lines is developed. Although modern spectroscopic surveys involve millions of spectra and give rise to highly multimodal posterior distributions, the proposed framework remains computationally efficient, admitting a parallelizable implementation suitable for large-scale inference.

Problem

• galaxy redshift estimation
• emission line detection
• spectroscopic data
• Bayesian inference
• large-scale inference

Innovation

• Bayesian framework
• emission line detection
• redshift estimation
• scalable inference
• multimodal posterior