WITHDRAWN: A scalable Bayesian approach to spectral line detection and galaxy redshift estimation

Music City Center 

Estimating galaxy redshifts is crucial for constraining key physical quantities like dark energy. Modern spectroscopic telescopes such as the James Webb Space Telescope (JWST) are producing massive amounts of high-resolution data that enable precise redshift estimation. However, this is only possible when spectral lines are present in the data, which is not known a priori. We adopt a fully Bayesian approach to estimate redshift, using Bayes factors to test for multiple spectral lines. The main challenge is computational, as the known physical constraints between redshift and spectral line signal intensities lead to a highly multimodal posterior distribution. To address this, we develop a fast Laplace approximation-based method that explicitly accounts for multimodality and apply it to new JWST spectra.

Bayes factors

Astrostatistics

Laplace approximation 

Section on Physical and Engineering Sciences