On the Precision of Full-spectrum Fitting of Simple Stellar Populations. I. Well-sampled Populations

TL;DR

This study assesses the accuracy of full-spectrum fitting for determining ages and metallicities of simple stellar populations using mock data, highlighting the influence of signal-to-noise ratio, wavelength range, and stellar evolution models.

Contribution

It provides a comprehensive analysis of the precision and systematic uncertainties in age and metallicity estimates from full-spectrum fitting across different stellar evolution models and observational conditions.

Findings

For S/N ≥ 50, age precision is about 0.1 dex in certain ranges.

Age uncertainties increase to ±0.3 dex in specific evolutionary phases.

Systematic differences between MIST and Padova models affect age predictions.

Abstract

We investigate the precision of the ages and metallicities of 21,000 mock simple stellar populations (SSPs) determined through full-spectrum fitting. The mock SSPs cover an age range of 6.8 $<$ log (age/yr) $<$ 10.2, for three wavelength ranges in the optical regime, using both Padova and MIST isochrone models. Random noise is added to the model spectra to achieve S/N ratios between 10 to 100 per wavelength pixel. We find that for S/N $\geq$ 50, this technique can yield ages of SSPs to an overall precision of $\Delta\,\mbox{log(age/yr)} \sim 0.1$ for ages in the ranges 7.0 $\leq$ log (age/yr) $\leq$ 8.3 and 8.9 $\leq$ log (age/yr) $\leq$ 9.4. For the age ranges of 8.3 $\leq$ log (age/yr) $\leq$ 8.9 and log (age/yr) $\geq$ 9.5, which have significant flux contributions from asymptotic giant branch (AGB) and red giant branch (RGB) stars, respectively, the age uncertainty rises to about $\pm 0.3$ dex. The precision of age and metallicity estimation using this method depends significantly on the S/N and the wavelength range used in the fitting. We quantify the systematic differences in age predicted by the MIST and Padova isochrone models, due to their different assumptions about stellar physics in various important (i.e., luminous) phases of stellar evolution, which needs to be taken in consideration when comparing ages of star clusters obtained using these popular models. Knowing the strengths and limitations of this technique is crucial in interpreting the results obtained for real star clusters and for deciding the optimal instrument setup before performing the observations.