Stellar Ages: A Code to Infer Properties of Stellar Populations

TL;DR

The paper introduces Stellar Ages, a statistical algorithm that accurately infers stellar population properties like age, metallicity, and extinction from magnitudes, combining individual star analysis with population constraints for improved precision.

Contribution

This work presents a novel framework that simultaneously estimates individual stellar ages and population parameters, enhancing accuracy over previous methods.

Findings

Successfully inferred the age of synthetic stellar populations.

Determined the age and progenitor mass of stars near supernova SN 2004dj.

Achieved median age estimate of log10(Age/yr)=7.19 with uncertainties.

Abstract

We present a novel statistical algorithm, Stellar Ages, which currently infers the age, metallicity, and extinction posterior distributions of stellar populations from their magnitudes. While this paper focuses on these parameters, the framework is readily adaptable to include additional properties, such as rotation, in future work. Historical age-dating techniques either model individual stars or populations of stars, often sacrificing population context or precision for individual estimates. Stellar Ages does both, combining the strengths of these approaches to provide precise individual ages for stars while leveraging population-level constraints. We verify the algorithm's capabilities by determining the age of synthetic stellar populations and actual stellar populations surrounding a nearby supernova, SN 2004dj. In addition to inferring an age, we infer a progenitor mass consistent with direct observations of the precursor star. The median age inferred from the brightest nearby stars is $\log_{10}$(Age/yr) = $7.19^{+0.10}_{-0.13}$, and its corresponding progenitor mass is $13.95^{+3.33}_{-1.96}$ $\text{M}_{\odot}$.