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arXiv 2410.11202astro-ph.SRastro-ph.GA
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基于LAMOST光谱巡天的类太阳恒星的距离与恒星参数估计

Distance and stellar parameter estimations of solar-like stars from the LAMOST spectroscopic survey

Yue-Yue Shen, A-Li Luo

备注：
17 pages, 18, figures, accepted by A&A
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AI总结：

提出CNN模型直接从LAMOST低分辨率光谱预测521,424颗类太阳恒星的距离及恒星参数，精度高且与现有研究一致，为银河系结构与系外行星研究提供可靠数据。

摘要：

背景。Gaia任务开启了恒星精密天体测量的新时代，从而彻底改变了我们对银河系的理解。然而，在距离太阳几千秒差距之外，视差测量变得不那么可靠，甚至在2 kpc以内，仍然存在具有较大不确定性的恒星。目标。我们的目标是确定来自LAMOST DR9的521,424颗类太阳恒星的距离和恒星参数；这些恒星在与Gaia核对时缺乏精确的距离测量（不确定性高于20%甚至没有任何距离估计）。方法。我们提出了一种卷积神经网络（CNN）模型，直接从低分辨率光谱预测绝对星等、颜色和恒星参数（T_eff，logg和[FeH]）。对于g波段信噪比（S/N_g）大于10的光谱，该模型对T_eff的精度达到85 K，logg达到0.07 dex，[Fe/H]达到0.06 dex，M_g达到0.25 mag，bp-rp达到0.03 mag。估计的距离的中位数分数误差为4%，标准差为8%。结果。我们将训练好的CNN模型应用于521,424颗类太阳恒星，以推导距离和恒星参数。与其他距离估计研究和光谱巡天相比，结果显示出良好的一致性。此外，我们从一个子样本中研究了银河系的金属丰度梯度，发现径向梯度范围在-0.05 < Delta{[Fe/H]}/Delta{R} < 0.0 dex/kpc之间，垂直梯度范围在-0.26 < Delta{[Fe/H]}/Delta{Z} < -0.07dex/kpc之间。结论。我们得出结论，我们的方法对于估计天体测量数据有限的类太阳恒星的距离和恒星参数是有效的。我们的测量对于银河系结构研究是可靠的，并有望对系外行星研究有用。

英文摘要：

Context. The Gaia mission has opened up a new era for the precise astrometry of stars, thus revolutionizing our understanding of the Milky Way. However, beyond a few kiloparseconds from the Sun, parallax measurements become less reliable, and even within 2 kpc, there still exist stars with large uncertainties. Aims. Our aim was to determine the distance and stellar parameters of 521,424 solar-like stars from LAMOST DR9; these stars lacked precise distance measurements (uncertainties higher than 20\% or even without any distance estimations) when checked with Gaia. Methods. We proposed a convolutional neural network (CNN) model to predict the absolute magnitudes, colors, and stellar parameters (T_eff, logg, and [FeH]) directly from low-resolution spectra. For spectra with signal-to-noise ratios at g band (S/N_g) greater than 10, the model achieves a precision of 85 K for T_eff, 0.07 dex for logg, 0.06 dex for [Fe/H], 0.25 mag for M_g, and 0.03 mag for bp-rp. The estimated distances have a median fractional error of 4% with a standard deviation of 8%. Results: We applied the trained CNN model to 521,424 solar-like stars to derive the distance and stellar parameters. Compared with other distance estimation studies and spectroscopic surveys, the results show good consistency. Additionally, we investigated the metallicity gradients of the Milky Way from a subsample, and find a radial gradient ranging from -0.05 < Delta{[Fe/H]}/Delta{R} < 0.0 dex/kpc and a vertical gradient ranging from -0.26 < Delta{[Fe/H]}/Delta{Z} < -0.07dex/kpc Conclusions. We conclude that our method is effective in estimating distances and stellar parameters for solar-like stars with limited astrometric data. Our measurements are reliable for Galactic structure studies and hopefully will be useful for exoplanet researches.

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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