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.
The provabgs Python package provides
- 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 is >100x faster than the original SPS model and 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 pipeline based on the zeus ensemble slice Markov Chain Monte Carlo (MCMC) sample.
For additional details see documentation and Hahn et al (2022)
To install the package, clone the github repo and use pip to install
# clone github repo
git clone https://github.com/changhoonhahn/provabgs.git
cd provabgs
# install
pip install -e . If you only plan to use provabgs with the neural emulators, then provabgs
does not require fsps. However, if you want to use the original SPS model,
you will need to install python-fsps. See python-fsps
documentation for installation
instruction.
If you're using provabgs on NERSC, see below for
some notes on installing FSPS on NERSC.
I've been running into some issues installing and using fsps on NERSC. e.g.
there's an import error with libgfotran.so.5. The following may resolve the problem...
module unload PrgEnv-intel
module load PrgEnv-gnu
Checkout the nb/example.ipybn
notebook for an example on conducting Bayesian SED modeling on galaxy
spectra using provabgs. It requires less than 10 lines of code and
about 10 minutes!
If you're interested in conducting Bayesian SED modeling on DESI spectra in particular, check out the nb/tutorial_desispec.ipynb notebook.
- ChangHoon Hahn (Princeton)
- Rita Tojeiro (St Andrews)
- Justin Alsing (Stockholm)
- James Kyubin Kwon (Berkeley)
If you have any questions or need help using the package, please raise a github issue, post a message on gitter, or contact me at changhoon.hahn@princeton.edu