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

TL;DR

This paper introduces a scalable Bayesian method for galaxy emission line detection and redshift estimation, addressing the challenge of unknown emission line presence in large spectroscopic datasets from modern telescopes.

Contribution

It presents a novel Bayesian framework that efficiently estimates galaxy redshifts and tests for emission lines simultaneously, suitable for large-scale spectroscopic surveys.

Findings

Framework handles highly multimodal posteriors efficiently.

Parallelizable implementation enables large-scale inference.

Method improves accuracy in redshift estimation with emission line detection.

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.