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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 |mocha|_, 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
----

- |chang|_ (Princeton)
- Rita Tojeiro (St Andrews)
- Justin Alsing (Stockholm) 
- James Kyubin Kwon (Berkeley) 

.. _provabgs: https://github.com/changhoonhahn/provabgs
.. |provabgs| replace:: ``provabgs``

.. _chang: https://changhoonhahn.github.io/
.. |chang| replace:: ChangHoon Hahn

.. _desi: http://desi.lbl.gov/
.. |desi| replace:: DESI 

.. _zeus: https://github.com/minaskar/zeus
.. |zeus| replace:: ``zeus`` 

.. _mocha: https://arxiv.org/abs/2203.07391
.. |mocha| replace:: Hahn *et al.* (2022b) 


.. toctree::
   :maxdepth: 2

   nersc