Deriving the star formation histories of galaxies from spectra with simulation-based inference

TL;DR

This paper presents a simulation-based inference method using machine learning to rapidly and accurately derive star formation histories and metallicities from galaxy spectra, enabling large-scale analysis of spectroscopic survey data.

Contribution

The authors develop a novel SBI workflow that efficiently infers galaxy properties from spectra, achieving high accuracy and speed, suitable for large upcoming surveys.

Findings

Achieved 0.97 R^2 score in estimating galaxy formation times.

Recovered known age-velocity dispersion relationships in real data.

Performed inference up to five orders of magnitude faster than existing methods.

Abstract

High-resolution galaxy spectra encode information about the stellar populations within galaxies. The properties of the stars, such as their ages, masses, and metallicities, provide insights into the underlying physical processes that drive the growth and transformation of galaxies over cosmic time. We explore a simulation-based inference (SBI) workflow to infer from optical absorption spectra the posterior distributions of metallicities and the star formation histories (SFHs) of galaxies (i.e. the star formation rate as a function of time). We generated a dataset of synthetic spectra to train and test our model using the spectroscopic predictions of the MILES stellar population library and non-parametric SFHs. We reliably estimate the mass assembly of an integrated stellar population with well-calibrated uncertainties. Specifically, we reach a score of $0.97\,R^2$ for the time at which a given galaxy from the test set formed $50\%$ of its stellar mass, obtaining samples of the posteriors in only $10^{-4}$\,s. We then applied the pipeline to real observations of massive elliptical galaxies, recovering the well-known relationship between the age and the velocity dispersion, and show that the most massive galaxies ($\sigma\sim300$ km/s) built up to 90\% of their total stellar masses within $1$\,Gyr of the Big Bang. The inferred properties also agree with the state-of-the-art inversion codes, but the inference is performed up to five orders of magnitude faster. This SBI approach coupled with machine learning and applied to full spectral fitting makes it possible to address large numbers of galaxies while performing a thick sampling of the posteriors. It will allow both the deterministic trends and the inherent uncertainties of the highly degenerated inversion problem to be estimated for large and complex upcoming spectroscopic surveys, such as DESI, WEAVE, or 4MOST.