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Improving Stellar Parameter and Abundance Determinations of Early B-Type Stars

机译:改善早期B型恒星的恒星参数和丰度测定

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In the past years we have made great efforts to reduce the statis-tical and systematic uncertainties in stellar parameter and chemical abundance determinations of early B-type stars. Both the construction of robust model atoms for non-LTE line-formation calculations and a novel self-consistent spec-tral analysis methodology were decisive to achieve results of unprecedented pre-cision. They were extensively tested and applied to high-quality spectra of stars from OB associations and the field in the solar neighborhood, covering a broad parameter range. Initially, most lines of hydrogen, helium and carbon in the optical/near-IR spectral range were reproduced simultaneously in a consistent way for the first time, improving drastically on the accuracy of results in pub-lished work. By taking additional ionization equilibria of oxygen, neon. silicon, and iron into account, uncertainties as low as ~1% in effective temperature, ~10% in surface gravity and ~20% in elemental abundances are achieved—compared to ~5-10%, ~25% and a factor ~2-3 using standard methods. Several sources of systematic errors have been identified by comparison of our analysis methods for early B-type stars with previously used standard techniques, e.g., the VLT-FLAMES survey of massive stars. Improvements in automatic analyses are strongly recommended for meaningful comparisons of spectroscopic stellar parameters and chemical abundances ("observational con-straints") with predictions of stellar and galactic chemical evolution models.
机译:在过去几年中,我们努力降低恒星参数和早期B型恒星的化学丰富测定中的Statis-TiCal和系统的不确定性。对于非LTE线形成计算的鲁棒模型原子的构造以及新颖的自我一致规范分析方法具有决定性,以实现前所未有的预触发结果的结果。它们被广泛地测试并应用于来自OB关联和太阳能邻域中的领域的恒星的高质量光谱,覆盖广泛的参数范围。最初,首次以一致的方式同时再现光/近红外光谱范围内的大多数氢气,氦和碳,同时再现,从而提高了PUB Living工作的结果的准确性。通过采取氧气,氖的额外电离平衡。硅和铁考虑,有效温度低至〜1%的不确定性,表面重力〜10%,元素丰富的〜20%达到 - 相比〜5-10%,〜25%和一个因子〜2 -3使用标准方法。通过比较我们的早期B型恒星的分析方法,例如使用先前使用的标准技术,例如具有预先使用的标准技术的若干系统误差来源..,大型恒星的VLT火焰调查。强烈建议对恒星和银河化学进化模型预测的光谱恒星参数和化学丰富的有意义比较(“观察与群”)的有意义的比较来强烈推荐自动分析的改进。

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