论文标题
锂在晚期恒星中的3D NLTE光谱线形成
3D NLTE spectral line formation of lithium in late-type stars
论文作者
论文摘要
可以使用准确的恒星锂丰度来阐明各种天体物理现象，例如大爆炸核合成，径向迁移，恒星年龄和恒星簇以及行星吞噬事件。我们提出了一个合成锂光谱的网格，该网格是在非本地热力学平衡（NLTE）中计算出的，跨越了三维（3D）流体动力学恒星大气模型的交错网格。该网格涵盖了610.4 nm，670.8 nm和812.6 nm的三条LI线，用于FGK-Type dwarfs和Giants的出色参数，跨越$ t _ {\ rm {eff}} = 4000 $ -7000 $ -7000 $ -7000 $ -7000 $ -7000 $ -7000 $ -7000 $ \ log g = 1.5 $ -5.5 $ -5.0， $ [\ rm {fe}/\ rm {h}] = -4.0 $ -0.5和$ \ textrm {a（li）} = -0.5 $ -4.0。我们发现，由于先前被忽视的NLTE效果，其较高的NLTE效应对紫外线锂电线阻止了紫外线，这对广泛的科学案例具有重要意义，因此我们的丰度校正比以前的工作更高0.15。我们得出了$ \ textrm {a（li）} = 0.96 \ pm 0.05 $的新的3D NLTE太阳能丰度，比常用值低0.09 dex。我们通过Breidablik套件公开获得合成光谱和丰度更正的网格。该软件包包括通过基于kriging（高斯过程回归）的方法来准确插入我们的网格到任意恒星参数的方法，MLP（多层感知器，一类完全连接的前馈神经网络）的NLTE校正和3D NLTE元素的序列为0等。
Accurately known stellar lithium abundances may be used to shed light on a variety of astrophysical phenomena such as Big Bang nucleosynthesis, radial migration, ages of stars and stellar clusters, and planet engulfment events. We present a grid of synthetic lithium spectra that are computed in non-local thermodynamic equilibrium (NLTE) across the STAGGER grid of three-dimensional (3D) hydrodynamic stellar atmosphere models. This grid covers three Li lines at 610.4 nm, 670.8 nm, and 812.6 nm for stellar parameters representative of FGK-type dwarfs and giants, spanning $T_{\rm{eff}}=4000$-7000 K, $\log g=1.5$-5.0, $[\rm{Fe}/\rm{H}] = -4.0$-0.5, and $\textrm{A(Li)} = -0.5$-4.0. We find that our abundance corrections are up to 0.15 dex more negative than in previous work, due to a previously overlooked NLTE effect of blocking of UV lithium lines by background opacities, which has important implications for a wide range of science cases. We derive a new 3D NLTE solar abundance of $\textrm{A(Li)} = 0.96 \pm 0.05$, which is 0.09 dex lower than the commonly used value. We make our grids of synthetic spectra and abundance corrections publicly available through the Breidablik package. This package includes methods for accurately interpolating our grid to arbitrary stellar parameters through methods based on Kriging (Gaussian process regression) for line profiles, and MLP (Multi-Layer Perceptrons, a class of fully connected feedforward neural networks) for NLTE corrections and 3D NLTE abundances from equivalent widths, achieving interpolation errors of the order 0.01 dex.
