The DESI PRObabilistic Value-Added Bright Galaxy Survey (PROVABGS) Mock Challenge
ChangHoon Hahn, K.J. Kwon, Rita Tojeiro, Malgorzata Siudek, Rebecca E., A. Canning, Mar Mezcua, Jeremy L. Tinker, David Brooks, Peter Doel, Kevin, Fanning, Enrique Gazta\~naga, Robert Kehoe, Martin Landriau, Aaron Meisner,, John Moustakas, Claire Poppett, Gregory Tarle

TL;DR
PROVABGS is a Bayesian framework for inferring detailed galaxy properties from DESI data, demonstrating improved accuracy and the importance of combined spectral and photometric analysis.
Contribution
This work introduces a novel Bayesian SED modeling approach for the PROVABGS catalog, validated on realistic simulations, highlighting the impact of priors and the benefits of joint spectral-photometric analysis.
Findings
Accurately infer galaxy properties from synthetic DESI data.
Priors significantly influence posterior estimates, especially for low S/N data.
Joint spectral and photometric analysis enhances property constraints.
Abstract
The PRObabilistic Value-Added Bright Galaxy Survey (PROVABGS) catalog will provide measurements of galaxy properties, such as stellar mass (), star formation rate (), stellar metallicity (), and stellar age (), for >10 million galaxies of the DESI Bright Galaxy Survey. Full posterior distributions of the galaxy properties will be inferred using state-of-the-art Bayesian spectral energy distribution (SED) modeling of DESI spectroscopy and Legacy Surveys photometry. In this work, we present the SED model, Bayesian inference framework, and methodology of PROVABGS. Furthermore, we apply the PROVABGS SED modeling on realistic synthetic DESI spectra and photometry, constructed using the L-GALAXIES semi-analytic model. We compare the inferred galaxy properties to the true galaxy properties of the simulation using a hierarchical Bayesian framework to…
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Taxonomy
TopicsSpectroscopy and Chemometric Analyses · Advanced Statistical Methods and Models · Data Visualization and Analytics
