SEDflow is an accelerated Bayesian SED modeling method that uses Amortized Neural Posterior Estimation (ANPE), a simulation-based inference method that employs neural networks to estimate the posterior probability distribution over the full range of observations. Once trained, it requires no additional model evaluations to estimate the posterior. SEDflow takes ∼1 second per galaxy to derive posteriors of the Hahn et al. (2022a) SED model parameters that are in excellent agreement with traditional Markov Chain Monte Carlo sampling results. SEDflow is ~100,000\times faster than convetional methods.
For additional details on SEDflow see documentation and Hahn & Melchior (2022).