# ChangHoon Hahn

**SEDflow**
—
python package for Bayesian SED modeling using Amortized Neural
Posterior Estimation, 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 and takes *1 second per galaxy* to estimate accurate
posteriors.

**provabgs**
—
python package for deriving physical properties of galaxies from
joint SED modeling of photometry and spectra. provabgs includes neural
emulators that perform Bayesian SED modeling 100x faster than other
methods. provabgs is currently being deployed on the Dark Energy
Spectroscopic Instrument Bright Galaxy Survey (DESI BGS).

**Molino Galaxy Catalogs**
—
75,000 mock galaxy catalogs constructed from full N-body simulations.
The mock catalogs are designed to quantify the information content of
any cosmological observable for a redshift-space galaxy sample. They
can be used to forecast how well your favorite observable can constrain
can constrain cosmological parameters
(e.g. the bispectrum).

**Quijote Simulations**
—
>43,000 full N-body simulations designed to quantify the information content of
cosmological observables and train machine learning algorithms.
Over a **petabyte** of data publicly available!

**pySpectrum**
—
Python package for measuring the power spectrum and bispectrum using
the Fast Fourier Transform (FFT)-based
Scoccimarro (2015)
estimator.

**LetsTalkAboutQuench**
—
Identify the star-forming sequence of galaxies in a data-driven
approach using Gaussian mixture models and Bayesian Information Criteria.
See Hahn et al. (2019)
for details.

Checkout my GitHub to see what I'm working on these days!