Data from Stellar Ages: A Code to Infer Properties of Stellar Populations

This data was used to validate a statistical algorithim Stellar Ages.

We present a novel statistical algorithm \textit{Stellar Ages}, which, at this time, infers the age, metallicity, and extinction posterior distributions of stellar populations from their magnitudes. One can easily extend the parameters to include other properties such as rotation. This algorithm uses joint probability density functions and a Gibbs Markov-chain-Monte-Carlo (MCMC) sampler to infer these posterior distributions. Additionally, \textit{Stellar Ages} can infer the ages for individual stars within a population, enabling more detailed analyses and opening up new means of investigation. 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 $13.95^{+3.33}_{-1.96}$ $\text{M}_{\odot}$.

本数据集用于验证名为Stellar Ages的统计算法。
本文提出了一款全新的统计算法Stellar Ages（恒星年龄算法），当前版本可通过恒星的星等推断恒星族群的年龄、金属丰度与消光后验分布。该算法的参数可轻松扩展，纳入自转等其他恒星属性。本算法借助联合概率密度函数与吉布斯马尔可夫链蒙特卡洛（MCMC）采样器来推断上述后验分布。此外，Stellar Ages算法还可对单个恒星族群内的单颗恒星年龄进行推断，支持更精细的分析工作，开辟了全新的研究路径。
研究团队通过测定合成恒星族群与近邻超新星SN 2004dj周围的真实恒星族群的年龄，验证了该算法的性能。除年龄推断外，本研究还得到了与该超新星前身星直接观测结果一致的前身星质量。通过近邻最亮恒星推断得到的年龄中位数为$log_{10}$(Age/yr) = $7.19^{+0.10}_{-0.13}$，对应的前身星质量为$13.95^{+3.33}_{-1.96}$ M☉。