Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation

TL;DR

This paper introduces SEDflow, a neural network-based method for rapid Bayesian inference of galaxy properties from spectral energy distribution data, drastically reducing computation time compared to traditional methods.

Contribution

The paper presents SEDflow, a novel amortized neural posterior estimation approach that enables fast, accurate Bayesian analysis of galaxy SEDs, scalable to billions of galaxies.

Findings

SEDflow produces posteriors in about 1 second per galaxy.

Posteriors from SEDflow agree well with traditional MCMC results.

Applied to 33,884 galaxies, SEDflow efficiently generates large-scale galaxy property posteriors.

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, ${\rm SED}{flow}$, an ANPE method to produce posteriors of the recent Hahn et al. (2022) SED model from optical photometry. ${\rm SED}{flow}$ takes ${\sim}1$ $second~per~galaxy$ 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 ${\rm SED}{flow}$ to 33,884 galaxies in the NASA-Sloan Atlas and publicly release their posteriors: see https://changhoonhahn.github.io/SEDflow.