SEDflow: Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation

SEDflow is an accelerated Bayesian SED modeling method that uses the  Hahn et al. (2022a) PROVABGS SED model and Amortized Neural Posterior Estimation (ANPE) to derive posterior probability distributions of galaxy properties from optical photometry. SEDflow is\(10^5\times\) faster than conventional Markov Chain Monte Carlo sampling methods and takes ~1 second per galaxy to obtain posteriors. This repository includes all of the data used to train, validate, and test SEDflow.

This repository also includes a value-added catalog with detailed physical properties of 33,884 galaxies in the NASA-Sloan Atlas (http://www.nsatlas.org/). The properties are inferred from optical photometry in the u, g, r, i, z bands using SEDflow. For more details on this catalog and SEDflow see the documentation and Hahn & Melchior (2022). 

For each galaxy, the catalog provides posteriors of: 

log_mstar: log10 of stellar mass

log_sfr_1gyr: log10 of average star formation rate over 1Gyr

log_z_mw: log10 of mass-weighted metallicity

beta1, beta2, beta3, beta4: coefficients of the non-negative matrix factorization (NMF) star formation history basis functions

fburst: fraction of stellar mass formed by a starburst event

tburst: time of the starburst event

log_gamma1, log_gamma2: log10 of coefficients of the NMF metallicity history basis functions

tau_bc: birth cloud optical depth

tau_ism: diffuse dust optical depth

n_dust: Calzetti (2001) dust index