论文标题
预测化学丰度精度
Forecasting Chemical Abundance Precision for Extragalactic Stellar Archaeology
论文作者
论文摘要
越来越强大的多重光谱设施有望在银河系（MW）以外的星系中为数百万恒星（MW）中的详细化学丰度模式。在这里，我们采用Cramér-Rao下限（CRLB）来预测可以测量41个电流（例如Keck，MMT，MMT，VLT，Desi）和计划的MW以外的金属罚款，低质量星的精确度，例如我们表明，在蓝光波长（$λ\ \ Lessim4500 $Å）处的中等分辨率（$ r \ lyssim5000 $）光谱镜检查可以使恢复2-4倍的元素是重新光谱光谱的2-4倍（$ 5000 \ limessimpimessimpimessim10000 $ $ $Å），并在$ simsim10000 $上（$）（$）（$）（$）（$ sims in Simel $Å）（$）（$）（$）将几个中子捕获元素的丰度限制为$ \ lyssim $ 0.3 dex。我们进一步表明，高分辨率（$ r \ gtrsim 20000 $），低s/n（$ \ sim $ 10 $ 10像素$^{ - 1} $）光谱包含丰富的丰度信息，当时使用完整的光谱拟合技术建模。我们证明，JWST/NIRSPEC和ELT可以恢复（i）$ \ sim $ 10和30个要素，用于整个本地组的金属贫困的红色巨人，以及（ii）[fe/h]和[$α$/h]和[$α$/fe]，用于与几个MPC的星系中已解决的星星，以及与温和整合时间的几个MPC。我们表明，精选的文献丰度在我们的CRLB的$ \ sim $ 2（或更高）之内。我们建议，在计划出色的光谱观察结果时，应使用CRLB。我们包括一个开源Python软件包\ Texttt {Chem-i-Calc}，该软件包允许用户计算CRLB以进行选择。
Increasingly powerful and multiplexed spectroscopic facilities promise detailed chemical abundance patterns for millions of resolved stars in galaxies beyond the Milky Way (MW). Here, we employ the Cramér-Rao Lower Bound (CRLB) to forecast the precision to which stellar abundances for metal-poor, low-mass stars outside the MW can be measured for 41 current (e.g., Keck, MMT, VLT, DESI) and planned (e.g., MSE, JWST, ELTs) spectrograph configurations. We show that moderate resolution ($R\lesssim5000$) spectroscopy at blue-optical wavelengths ($λ\lesssim4500$ Å) (i) enables the recovery of 2-4 times as many elements as red-optical spectroscopy ($5000\lesssimλ\lesssim10000$ Å) at similar or higher resolutions ($R\sim 10000$) and (ii) can constrain the abundances of several neutron capture elements to $\lesssim$0.3 dex. We further show that high-resolution ($R\gtrsim 20000$), low S/N ($\sim$10 pixel$^{-1}$) spectra contain rich abundance information when modeled with full spectral fitting techniques. We demonstrate that JWST/NIRSpec and ELTs can recover (i) $\sim$10 and 30 elements, respectively, for metal-poor red giants throughout the Local Group and (ii) [Fe/H] and [$α$/Fe] for resolved stars in galaxies out to several Mpc with modest integration times. We show that select literature abundances are within a factor of $\sim$2 (or better) of our CRLBs. We suggest that, like ETCs, CRLBs should be used when planning stellar spectroscopic observations. We include an open source python package, \texttt{Chem-I-Calc}, that allows users to compute CRLBs for spectrographs of their choosing.
