使用神经网络方法和星斑依赖模型来预测年轻恒星的有效温度和年龄
Marco Tarantino1, Loredana Prisinzano2, Nicoletta D'Angelo1
1University of Palermo, Department of Economics, Business and Statistics, Palermo, Italy.
PloS one
|December 17, 2025
概括
这项研究使用神经网络从光度数据中预测主顺序前恒星温度,使大恒星群的恒星年龄能够准确地确定,并揭示年轻星团中的年龄分布.
科学领域:
- 天文学和天体物理学
- 恒星进化 恒星进化
- 计算天体物理学 计算天体物理学
背景情况:
- 准确的恒星有效温度对于使用同位素和星点模型确定恒星年龄至关重要.
- 主序前恒星需要精确的温度测量来估计年龄,特别是在年轻星团中.
- 现有的导出有效温度的方法通常依赖于光谱数据,限制样本大小.
研究的目的:
- 开发一种可靠的统计方法,使用易于获得的光度数据来预测主序前恒星的有效温度.
- 为了能够准确地确定只有光度数据存在的大型恒星群的年龄.
- 为了研究年轻星团和恒星形成区域的恒星特性和年龄分布.
主要方法:
- 使用光谱温度 (Gaia-ESO调查) 作为响应变量训练一个神经网络模型.
- 使用Gaia DR3和2MASS目录中的光度数据作为温度预测的解释变量.
- 应用预测的温度来构建赫茨普隆 - 拉塞尔图,并使用同位素和星点模型插曲恒星年龄.
主要成果:
- 神经网络模型准确地预测了低质量主序前恒星 (<7000 K) 的有效温度.
- 预测的温度给出了恒星年龄,与基于光谱的年龄和文献值非常一致.
- 结合星点进化模型可以提高恒星年龄预测的准确性.
- 最年轻星团内内在年龄扩散的证据表明有多个恒星形成事件.
结论:
- 对于大型恒星群体,已经建立了一种可靠的摄影测量方法来有效预测温度.
- 该研究为年轻星团提供了准确的年龄估计,支持使用先进的恒星模型.
- 这些发现表明,一些年轻的星团表现出内在的年龄分布,表明复杂的形成历史.
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