论文标题

一种纠正$ T_E $ $的大规模金属关系的新方法

A novel approach to correcting $T_e$-based mass-metallicity relations

论文作者

Cameron, Alex J., Katz, Harley, Rey, Martin P.

论文摘要

从电子温度(以下$ t_e $ -Method)中得出氧气丰度是用于金属外层化金属性研究的金标准。然而,各个HII区域内以及整个星系中不同HII区域内的未解决温度波动可以偏向金属性估计低,其大小取决于基础和通常未知的温度分布。使用玩具模型,我们确认使用从[O III] $λ$ 4363 / $λ$ 5007或[O II] $λλ$ 7320,7330 / [O II] $λλ$ 3727的比率('$ 3727)('$ 3727)的情况下,计算$ T_E $的金属计算。当存在温度波动时。相比之下,使用不可观察的“线温度”($ t _ {\ rm line} $),它提供平均电子和离子密度加权发射率可产生准确的金属性估计。为了纠正低质量星系中的这种偏见,我们演示了基于高分辨率(4.5 pc)的t_ratio和t_line之间关系的示例校准,对矮星系的RAMSES-RTZ模拟了,该矮星系是自言自语的,该矮星系在银河系中的多个HII区域和离子温度分布的形成。将这种校正应用于质量金属关系的低质量端,平均将其归一化提高了0.18 DEX,并将其斜率从0.87升至0.58,突显了未来的研究需要考虑并纠正这种偏见。

Deriving oxygen abundances from the electron temperature (hereafter the $T_e$-method) is the gold-standard for extragalactic metallicity studies. However, unresolved temperature fluctuations within individual HII regions and across different HII regions throughout a galaxy can bias metallicity estimates low, with a magnitude that depends on the underlying and typically unknown temperature distribution. Using a toy model, we confirm that computing $T_e$-based metallicities using the temperature derived from the [O III] $λ$4363/$λ$5007 or [O II] $λλ$7320,7330 / [O II] $λλ$3727 ratio ('ratio temperature'; $T_{\rm ratio}$) results in an underprediction of metallicity when temperature fluctuations are present. In contrast, using the unobservable 'line temperatures' ($T_{\rm line}$) that provide the mean electron and ion density-weighted emissivity yield an accurate metallicity estimate. To correct this bias in low-mass galaxies, we demonstrate an example calibration of a relation between T_ratio and T_line based on a high-resolution (4.5 pc) RAMSES-RTZ simulation of a dwarf galaxy that self-consistently models the formation of multiple HII regions and ion temperature distribution in a galactic context. Applying this correction to the low-mass end of the mass-metallicity relation shifts its normalization up by 0.18 dex on average and flattens its slope from 0.87 to 0.58, highlighting the need for future studies to account for, and correct, this bias.

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