Testing a New Star Formation History Model from Principal Component Analysis to Facilitate Spectral Synthesis Modeling

TL;DR

This paper introduces a PCA-based star formation history model that effectively captures diverse galaxy histories and improves spectral synthesis accuracy, offering a flexible and reliable tool for galaxy property inference.

Contribution

It develops a PCA-derived SFH model trained on simulations, demonstrating its superior flexibility and accuracy over traditional models in spectral synthesis.

Findings

PCA-based models effectively describe complex simulated SFHs.

Including recent SFH features improves spectral reproduction.

PCA+step model balances simplicity and accuracy.

Abstract

The spectrum of a galaxy is a complicated convolution of many properties of the galaxy, such as the star formation history (SFH), initial mass function, and metallicity. Inferring galaxy properties from the observed spectrum via spectral synthesis modeling is thus challenging. In particular, a simple yet flexible model for the SFH is required to obtain unbiased inferences. In this paper, we use SFHs from the IllustrisTNG and EAGLE simulations to test SFH models in terms of their capability of describing the simulated SFHs and the spectra generated from them. In addition to some commonly used SFH models ($\Gamma$, $\tau$, and nonparametric), we also examine a model developed from principal component analysis (PCA), trained by a set of SFHs from IllustrisTNG. We find that when using the first five principal components (eigenhistories), the PCA-based models can achieve a good balance between simplicity and accuracy. Among the models tested, the PCA-based model provides high flexibility, by capturing diverse and complex simulated SFHs. To accurately reproduce spectra generated from the simulated SFHs, it is necessary to have a degree of freedom to describe the most recent SFH (e.g., a step function covering the age of 0 - 0.3 Gyr). Overall, the PCA+step model performs well in capturing the diversity of SFHs and reproducing the associated spectra, suggesting it is a promising and reliable approach for spectral synthesis modeling.