论文标题
Odusseas：一种机器学习工具，用于得出M矮星的有效温度和金属性
ODUSSEAS: A machine learning tool to derive effective temperature and metallicity for M dwarf stars
论文作者
论文摘要
目标。在恒星和系外行星表征的领域，M矮星的光谱参数的推导非常重要。这项工作的目的是创建自动计算工具，能够通过使用其光谱来快速而可靠地得出t $ _ {\ mathrm {eff}} $和[fe/h]的t $ _ {\ mathrm {eff}} $和[fe/h]的光谱，这些光谱可以通过不同的光谱仪以不同的分辨率获得。 方法。 odusseas（使用恒星光谱能量吸收形状的观察矮人）基于对伪当量宽度的测量，用于超过4000种恒星吸收线，以及使用机器学习python package the scikit-learn“ Scikit-learn”，以预测Stellar parameter scikit-learn”。 结果。我们表明，我们的工具能够以高精度准确地得出参数，对于t $ _ {\ mathrm {eff}}} $，精度错误约为30 k，[fe/h]的精度错误。对于分辨率为48000至115000和SNR的光谱，结果是一致的。
Aims. The derivation of spectroscopic parameters for M dwarf stars is very important in the fields of stellar and exoplanet characterization. The goal of this work is the creation of an automatic computational tool, able to derive quickly and reliably the T$_{\mathrm{eff}}$ and [Fe/H] of M dwarfs by using their optical spectra, that can be obtained by different spectrographs with different resolutions. Methods. ODUSSEAS (Observing Dwarfs Using Stellar Spectroscopic Energy-Absorption Shapes) is based on the measurement of the pseudo equivalent widths for more than 4000 stellar absorption lines and on the use of the machine learning Python package "scikit-learn" for predicting the stellar parameters. Results. We show that our tool is able to derive parameters accurately and with high precision, having precision errors of ~30 K for T$_{\mathrm{eff}}$ and ~0.04 dex for [Fe/H]. The results are consistent for spectra with resolutions between 48000 and 115000 and SNR above 20.
