Surrogate modelling the Baryonic Universe II: on forward modelling the colours of individual and populations of galaxies

TL;DR

This paper investigates how unresolved star formation variability affects galaxy colour predictions, introducing a metric for burstiness and demonstrating that simple models suffice for population colours but complex models are needed for individual galaxy accuracy.

Contribution

It develops a new burstiness metric, constructs adjustable SFH models, and provides a fitting function linking variability to colour prediction precision.

Findings

Higher star formation variability degrades colour prediction accuracy.

Metallicity and dust variability have negligible impact on colours.

Simple models can accurately reproduce population colours, complex models needed for individual galaxies.

Abstract

Among the properties shaping the light of a galaxy, the star formation history (SFH) is one of the most challenging to model due to the variety of correlated physical processes regulating star formation. In this work, we leverage the stellar population synthesis model FSPS, together with SFHs predicted by the hydrodynamical simulation IllustrisTNG and the empirical model UNIVERSEMACHINE, to study the impact of star formation variability on galaxy colours. We start by introducing a model-independent metric to quantify the burstiness of a galaxy formation model, and we use this metric to demonstrate that UNIVERSEMACHINE predicts SFHs with more burstiness relative to IllustrisTNG. Using this metric and principal component analysis, we construct families of SFH models with adjustable variability, and we show that the precision of broad-band optical and near-infrared colours degrades as the level of unresolved short-term variability increases. We use the same technique to demonstrate that variability in metallicity and dust attenuation presents a practically negligible impact on colours relative to star formation variability. We additionally provide a model-independent fitting function capturing how the level of unresolved star formation variability translates into imprecision in predictions for galaxy colours; our fitting function can be used to determine the minimal SFH model that reproduces colours with some target precision. Finally, we show that modelling the colours of individual galaxies with percent-level precision demands resorting to complex SFH models, while producing precise colours for galaxy populations can be achieved using models with just a few degrees of freedom.