# Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation

arXiv: https://arxiv.org/abs/2203.07391 · HTML: https://mlsanity.com/paper/2203.07391/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 68 references that are in the map.
- First posted on arXiv 14 Mar 2022.

## Builds on (most cited first, 5 of 5)

- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [Automatic Posterior Transformation for Likelihood-Free Inference](https://mlsanity.com/paper/1905.07488/) (2019)
- [Mining gold from implicit models to improve likelihood-free inference](https://mlsanity.com/paper/1805.12244/) (2018)
- [Real-Time Likelihood-Free Inference of Roman Binary Microlensing Events with Amortized Neural Posterior Estimation](https://mlsanity.com/paper/2102.05673/) (2021)
- [Accelerating Approximate Bayesian Computation with Quantile Regression: Application to Cosmological Redshift Distributions](https://mlsanity.com/paper/1707.07498/) (2017)

## Built on by (most cited first, 7 of 7)

- [Simulation-Based Inference: A Practical Guide](https://mlsanity.com/paper/2508.12939/) (2025)
- [sbi reloaded: a toolkit for simulation-based inference workflows](https://mlsanity.com/paper/2411.17337/) (2024)
- [Domain-Adaptive Neural Posterior Estimation for Strong Gravitational Lens Analysis](https://mlsanity.com/paper/2410.16347/) (2024)
- [SimBIG: Field-level Simulation-Based Inference of Galaxy Clustering](https://mlsanity.com/paper/2310.15256/) (2023)
- [nbi: the Astronomer's Package for Neural Posterior Estimation](https://mlsanity.com/paper/2312.03824/) (2023)
- [Monte Carlo Techniques for Addressing Large Errors and Missing Data in Simulation-based Inference](https://mlsanity.com/paper/2211.03747/) (2022)
- [Stellar Spectra Fitting with Amortized Neural Posterior Estimation and nbi](https://mlsanity.com/paper/2312.05687/) (2023)

## Abstract (arXiv)

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