SEDflow: Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation

<p><a href="http://changhoonhahn.github.io/SEDflow">SEDflow</a>&nbsp;is an accelerated&nbsp;Bayesian SED modeling method that uses the&nbsp; <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220201809H/abstract">Hahn et al. (2022a)</a>&nbsp;PROVABGS SED model and Amortized Neural Posterior Estimation (ANPE) to derive posterior probability distributions&nbsp;of galaxy properties from optical photometry.&nbsp;SEDflow is<span class="math-tex">\(10^5\times\)</span>&nbsp;faster than conventional&nbsp;Markov Chain Monte Carlo sampling methods and takes ~1 second per galaxy to obtain posteriors.&nbsp;This repository includes all of the data used to train, validate, and test SEDflow.</p> <p>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&nbsp;properties are inferred from optical photometry in the <em>u, g, r, i, z</em> bands using SEDflow. For more details on this catalog and SEDflow&nbsp;see the <a href="http://changhoonhahn.github.io/SEDflow">documentation</a> and Hahn &amp; Melchior (2022).&nbsp;</p> <p>For each galaxy, the catalog provides posteriors of:&nbsp;</p> <ul> <li>log_mstar: log10 of stellar mass</li> <li>log_sfr_1gyr: log10 of average star formation rate over 1Gyr</li> <li>log_z_mw: log10 of mass-weighted metallicity</li> <li>beta1, beta2, beta3, beta4: coefficients of the non-negative matrix factorization (NMF) star formation history basis functions</li> <li>fburst: fraction of stellar mass formed by a starburst event</li> <li>tburst: time of the starburst event</li> <li>log_gamma1, log_gamma2: log10 of coefficients of the NMF metallicity history basis functions</li> <li>tau_bc: birth cloud optical depth</li> <li>tau_ism:&nbsp;diffuse dust&nbsp;optical depth</li> <li>n_dust: Calzetti (2001) dust index</li> </ul>