EAS2024
For this purpose, we measure the metallicities and star formation histories (SFHs) of galaxies, i.e. star formation rate as a function of time, from their optical absorption spectra, exploring an amortised implicit inference approach to estimate the posterior distributions.
Using the spectroscopic predictions of the MILES stellar population library and non-parametric SFHs, we generate a sample of synthetic spectra to train and test our model in a simulation-based inference workflow.
We reliably estimate the mass assembly of an integrated stellar population with well-calibrated uncertainties. Specifically, we reach a $0.97\,R^2$ score of accuracy 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 apply 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) build up to 90\% of their total stellar masses $1$\,Gyr after the Big Bang. The measurements also agree with the state-of-the-art inversion codes, but the inference is performed up to five orders of magnitude faster.
This machine learning-based implicit inference applied to full spectral fitting makes it possible to address large number of galaxies, while performing a thick sampling of the posteriors. It allows to estimate both the deterministic trends and the inherent uncertainties of the highly degenerated inversion problem, so far inaccessible, dealing with the size and complexity of upcoming spectroscopic surveys such as DESI, WEAVE or 4MOST.