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!