Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation

Abstract

State-of-the-art spectral energy distribution (SED) analyses use a Bayesian framework to infer the physical properties of galaxies from observed photometry or spectra. They require sampling from a high-dimensional space of SED model parameters and take $>10-100$ CPU hours per galaxy, which renders them practically infeasible for analyzing the $billions$ of galaxies that will be observed by upcoming galaxy surveys ($e.g.$ DESI, PFS, Rubin, Webb, and Roman). In this work, we present an alternative scalable approach to rigorous Bayesian inference using Amortized Neural Posterior Estimation (ANPE). ANPE is 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. We present, and publicly release, $\mathrm{SED}flow$, an ANPE method to produce posteriors of the recent Hahn et al. (2022) SED model from optical photometry. $\mathrm{SED}flow$ takes $\sim 1$ $secondpergalaxy$ to obtain the posterior distributions of 12 model parameters, all of which are in excellent agreement with traditional Markov Chain Monte Carlo sampling results. We also apply $\mathrm{SED}flow$ to 33,884 galaxies in the NASA-Sloan Atlas and publicly release their posteriors: see https://changhoonhahn.github.io/SEDflow.

21 pages, 5 figures; submitted to ApJ; code available at https://changhoonhahn.github.io/SEDflow

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Cited by in corpus (4)

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• Stochastic Modelling of Star Formation Histories III. Constraints from Physically-Motivated Gaussian Processes
• DSPS: Differentiable Stellar Population Synthesis