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arXiv 2409.07523astro-ph.SRastro-ph.GAastro-ph.IMcs.LG
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利用神经网络模型从锂等值宽度估算恒星年龄：EAGLES扩展研究

Using Neural Network Models to Estimate Stellar Ages from Lithium Equivalent Widths: An EAGLES Expansion

Keele University（基尔大学）

George Weaver, Robin D. Jeffries, Richard J. Jackson

备注：
Accepted for publication in Monthly Notices of the Royal Astronomical Society. Code available at https://github.com/robdjeff/eagles. Electronic tables are available from the author
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AI总结：

本研究提出基于人工神经网络的冷恒星年龄估算模型，利用锂等值宽度和有效温度输入，在6200颗疏散星团恒星样本上训练以校准EAGLES模型，改进了锂凹陷等模拟效果，相关方法已纳入2.0版EAGLES软件。

摘要：

我们提出了一种用于冷恒星（3000 < 有效温度Teff / K < 6500）光球层锂耗散的人工神经网络（ANN，Artificial Neural Network）模型，该模型以Li I 6708Å等值宽度（LiEW）和有效温度数据为输入，输出年龄的估算值与概率分布。\n该模型在Gaia-ESO光谱巡天中观测到的、来自52个疏散星团的6200颗恒星的相同样本上训练而成，用于校准此前发表的解析EAGLES模型，适用年龄范围为2-6000百万年（Myr），金属丰度范围为-0.3 < [Fe/H] < 0.2。ANN的额外灵活性带来了若干改进，包括更好地模拟年龄<50 Myr、有效温度约3500K时的“锂凹陷”现象，以及所有年龄段下LiEW的固有弥散。\n在年龄>1吉年（Gyr）时，年龄区分度差的问题仍然存在，这证实仅靠增加模型灵活性不足以完全表征LiEW-年龄-有效温度之间的关系，也表明存在其他天体物理参数的影响。本文讨论了将自转、吸积和表面重力等参数纳入模型的扩展方案，而ANN的使用意味着这些参数以及更灵活的LiEW弥散函数形式可以更轻松地纳入未来的迭代版本中。我们的方法和ANN模型已收录在更新的2.0版EAGLES软件中。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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