The PRObabilistic Value-Added Bright Galaxy Survey (PROVABGS) Catalog¶
The 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 these galaxy properties will be inferred using state-of-the-art Bayesian spectral energy distribution (SED) modeling of DESI spectroscopy and photometry. For further details on the PROVABGS SED modeling, checkout out Hahn et al. (2022b), our mock challenge paper where we applied the PROVABGS SED modeling on synthetic DESI observations.
provabgs
pipeline¶
All of the SED modeling tools for PROVABGS are available in the provabgs
Python package. The package includes:
a state-of-the-art stellar population synthesis (SPS) model based on non-parametric prescription for star formation history, a metallicity history that varies over the age of the galaxy, and a flexible dust prescription.
a neural network emulator (Kwon et al. in prep) for the SPS model that enables accelerated inference. Full posteriors of the 12 SPS parameters can be derived in ~10 minutes. The emulator is currently designed for galaxies from 0 < z < 0.6.
a Bayesian inference based on the
zeus
ensemble slice Markov Chain Monte Carlo (MCMC) sampler.
Catalog Data Releases¶
PROVABGS Early Data Release coming soon!
Team¶
ChangHoon Hahn (Princeton)
Rita Tojeiro (St Andrews)
Justin Alsing (Stockholm)
James Kyubin Kwon (Berkeley)