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Accelerated Bayesian SED Modeling
=================================

State-of-the-art SED analyses use a Bayesian framework to infer the physical properties of 
galaxies from observed photometry or spectra. 
They require sampling from a high-dimensional space of SED model parameters and take 
>10-100 CPU hours per galaxy.  
This makes them practically infeasible for analyzing the billions of galaxies that will 
be observed by upcoming galaxy surveys (e.g. DESI, PFS, Rubin, Webb, and Roman).

``SEDflow`` enables *scalable* Bayesian SED modeling using Amortized Neural Posterior Estimation 
(ANPE), a simulation-based inference method that employs neural networks 
to estimate the posterior over the full range of observations. 
Once trained, ``SEDflow`` requires no additional model evaluations to estimate the posterior. 

``SEDflow`` takes *∼1 second per galaxy* to obtain the posteriors of the |provabgs|_ SED 
model parameters, all of which are in excellent agreement with traditional Markov Chain Monte 
Carlo sampling results. For more details, check out |sedflow|_.

``PROVABGS`` SED Model
----------------------
``SEDflow`` applies ANPE to SED modeling using the recent |provabgs|_ SED model, 
the state-of-the-art SPS model of the |desi|_ PRObabilistic Value-Added Bright 
Galaxy Survey (``PROVABGS``) catalog. 
The SED of a galaxy is modeled as a composite of stellar populations defined by 
stellar evolution theory, its star formation and chemical enrichment histories 
(SFH and ZH), and dust attenuation. 
The |provabgs|_ model  utilizes a non-parametric SFH with a starburst, a 
non-parametric ZH that varies with time, and a flexible dust attenuation 
prescription.

NSA ``SEDflow`` Catalog
-----------------------
We apply ``SEDflow`` to 33,884 galaxies in the NASA-Sloan Atlas and construct 
a probabilistic value-added catalog. 
For more details on the catalog and how to download it, see [:ref:`datamodel`] 

Authors
-------
|chang|_ and |peter|_.

Questions or Feedback
---------------------
If you have any questions or feedback, please feel free to reach out at changhoon.hahn@princeton.edu

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

.. _peter: https://pmelchior.net/
.. |peter| replace:: Peter Melchior 

.. _sbi: https://github.com/mackelab/sbi/
.. |sbi| replace:: ``sbi``

.. _provabgs: https://ui.adsabs.harvard.edu/abs/2022arXiv220201809H
.. |provabgs| replace:: Hahn *et al.* (2022a) 

.. _sedflow: https://arxiv.org/abs/2203.07391
.. |sedflow| replace:: Hahn & Melchior (2022) 

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


.. toctree::
   :maxdepth: 1
    
   training
   datamodel