DIGS: deep inference of galaxy spectra with neural posterior estimation

Abstract

With the advent of billion-galaxy surveys with complex data, the need of the hour is to efficiently model galaxy spectral energy distributions (SEDs) with robust uncertainty quantification. The combination of Simulation-Based inference (SBI) and amortized Neural Posterior Estimation (NPE) has been successfully used to analyse simulated and real galaxy photometry both precisely and efficiently. In this work, we utilise this combination and build on existing literature to analyse simulated noisy galaxy spectra. Here, we demonstrate a proof-of-concept study of spectra that is a) an efficient analysis of galaxy SEDs and inference of galaxy parameters with physically interpretable uncertainties; and b) amortized calculations of posterior distributions of said galaxy parameters at the modest cost of a few galaxy fits with MCMC methods. We utilise the SED generator and inference framework Prospector to generate simulated spectra, and train a dataset of 2x10^6 spectra (corresponding to a 5-parameter SED model) with NPE. We show that SBI -- with its combination of fast and amortized posterior estimations -- is capable of inferring accurate galaxy stellar masses and metallicities. Our uncertainty constraints are comparable to or moderately weaker than traditional inverse-modeling with Bayesian MCMC methods (e.g., 0.17 and 0.26 dex in stellar mass and metallicity for a given galaxy, respectively). We also find that our inference framework conducts rapid SED inference (0.9-1.2x10^5 galaxy spectra via SBI/SNPE at the cost of 1 MCMC-based fit). With this work, we set the stage for further work that focuses of SED fitting of galaxy spectra with SBI, in the era of JWST galaxy survey programs and the wide-field Roman Space Telescope spectroscopic surveys.

Stochastic Modeling of Star Formation Histories. III. Constraints from Physically Motivated Gaussian Processes

“…We then estimate the posterior distribution of model parameters conditioned on an ensemble of observations, with results shown in Figure 19. Using a procedure similar to Hahn & Melchior (2022) and related papers (Khullar et al 2022;Legin et al 2023;Lemos et al 2023;Wang et al 2023), we implement SBI combined with amortized neural posterior estimation to determine that ensemble observations of about 30 galaxies allow us to constrain the timescales and scatter enough to differentiate between the toy models in this paper. Adding additional systematics like variable metallicity or observational noise broadens the posteriors, but nevertheless allows us to distinguish between the toy models and obtain constraints on the timescales, as shown in Figure 20.…”

PopSED: Population-level Inference for Galaxy Properties from Broadband Photometry with Neural Density Estimation

“…Using traditional SED fitting methods, analyzing 10 5 galaxies will take up to 2 × 10 6 CPU hr. Even with the development of accelerated SED fitting (e.g., Alsing et al 2020;Hearin et al 2023;Khullar et al 2022;Wang et al 2023), an analysis of 10 5 galaxies will still take up to ∼10 3 GPU hr. POPSED is able to recover the posterior of the population distribution for ∼10 5 galaxies within ∼10 GPU hr, 100 times faster than the SBIbased methods.…”

“…This problem has been partially mitigated by recent developments in accelerating SED modeling by emulating the SPS models with neural networks (Alsing et al 2020), building differentiable SPS models with a high-performance library (Hearin et al 2023), and speeding up the sampling by using amortized simulation-based inference (SBI; Khullar et al 2022;Wang et al 2023). However, some of these methods need sophisticated training and are costly to retrain when adapting to different SPS models or noise properties.…”

SBI++: Flexible, Ultra-fast Likelihood-free Inference Customized for Astronomical Applications

“…Simulation-based inference (SBI), which uses normalizing flows to learn posterior densities directly, is the ideal candidate. Several recent works have already adopted such neural density estimators to analyze astrophysical data (e.g., Alsing et al 2019;Green et al 2020;Dax et al 2021;Zhang et al 2021;Khullar et al 2022;Leja et al 2022;Ting & Weinberg 2022; see Cranmer et al 2020 for a recent review). However, the drastically increased evaluation speed of SBI is accompanied by an inflexibility: the data to be modeled must have properties identical to those of the training data, including nearly identical noise properties, free parameters, exact priors, and so forth.…”