用神经网络从锂线宽推算恒星年龄，提升早期演化和分散性的建模精度。
Using Neural Network Models to Estimate Stellar Ages from Lithium Equivalent Widths: An EAGLES Expansion
- 构建神经网络模型，输入有效温度与锂线等效宽度预测年龄分布
- 在50百万年以内更好地刻画锂耗尽谷区，改善全龄段数据离散度拟合
- 可扩展纳入自转、吸积等参数，适合研究恒星演化与测年方法的学者
我们提出一种人工神经网络（ANN）模型，用于冷却恒星（3000 < Teff / K < 6500）光球锂耗尽的建模，基于Li I 6708Å等效宽度（LiEW）和有效温度数据，输出年龄估计及其概率分布。模型训练基于来自52个疏散星团的6200颗恒星样本，来自盖亚-埃索光谱巡天，校准了先前发布的解析模型EAGLES，覆盖年龄2 - 6000 Myr及-0.3 < [Fe/H] < 0.2范围。相比原模型，该神经网络提升了对<50 Myr时在Teff ~ 3500K处的“锂耗尽谷”以及全年龄段锂线宽内在离散度的建模能力。然而在>1 Gyr时仍存在年龄分辨困难问题，表明仅增加建模灵活性不足以完全刻画LiEW-年龄-有效温度关系，暗示需引入更多天体物理参数。文中讨论将自转、吸积、表面重力等纳入模型的可能性，且由于使用神经网络，未来可更便捷地集成这些因素及更灵活的锂线宽分布形式。本文方法与神经网络模型已集成于更新版EAGLES软件2.0中。
原文摘要 · Abstract (English)
We present an Artificial Neural Network (ANN) model of photospheric lithium depletion in cool stars (3000 < Teff / K < 6500), producing estimates and probability distributions of age from Li I 6708A equivalent width (LiEW) and effective temperature data inputs. The model is trained on the same sample of 6200 stars from 52 open clusters, observed in the Gaia-ESO spectroscopic survey, and used to calibrate the previously published analytical EAGLES model, with ages 2 - 6000 Myr and -0.3 < [Fe/H] < 0.2. The additional flexibility of the ANN provides some improvements, including better modelling of the "lithium dip" at ages < 50 Myr and Teff ~ 3500K, and of the intrinsic dispersion in LiEW at all ages. Poor age discrimination is still an issue at ages > 1 Gyr, confirming that additional modelling flexibility is not sufficient to fully represent the LiEW - age - Teff relationship, and suggesting the involvement of further astrophysical parameters. Expansion to include such parameters - rotation, accretion, and surface gravity - is discussed, and the use of an ANN means these can be more easily included in future iterations, alongside more flexible functional forms for the LiEW dispersion. Our methods and ANN model are provided in an updated version 2.0 of the EAGLES software.
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