arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2002.09367astro-ph.SR
收藏

ODUSSEAS:一个机器学习工具,用于推导M矮星的有效温度和金属丰度

ODUSSEAS: A machine learning tool to derive effective temperature and metallicity for M dwarf stars

A. Antoniadis-Karnavas, S. G. Sousa, E. Delgado-Mena, N. C. Santos, G. D. C. Teixeira, V. Neves

摘要:

目标。M型矮星光谱参数的推导在恒星和系外行星表征领域具有重要意义。这项工作的目标是创建一个自动计算工具,能够快速和可靠地推导出的T $_{\mathrm{eff}}$和[Fe/H]的M矮星通过使用它们的光谱,可以得到不同的光谱仪具有不同的分辨率。方法. ODUSSEAS(Observing Dwarfs Using Stellar Spectroscopic Energy Absorgy-Absorption)基于对4000多条恒星吸收线的伪等效宽度的测量,并使用机器学习Python包“scikit-learn”来预测恒星参数。结果我们表明,我们的工具是能够准确地获得参数,具有高精度,具有约30 K的T $_{\mathrm{eff}}$和~0.04 dex的[Fe/H]的精度误差。对于分辨率在48000和115000之间并且SNR高于20的光谱,结果是一致的。

英文摘要:

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.

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。

↑