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arXiv 0705.2726astro-ph
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空间分布电离源对HII区温度结构的影响

The effects of spatially distributed ionisation sources on the temperature structure of HII region

B. Ercolano, N. Bastian, G. Stasinska

摘要:

对恒星形成区的空间分辨研究表明,球形几何的假设在大多数情况下是不现实的,一个主要的复杂因素是气体被多个非中心位置的恒星或星团电离。我们试图隔离多个非中心位置的恒星的HII区域的温度和电离结构的影响,通过使用三维蒙特卡罗光电离代码MOCASSIN的三维光电离模型的建设。我们发现,真正的温度波动,由于恒星分布(相对于大规模的温度梯度,由于其他气体的性质)是小的,在所有情况下,并不是一个显着的原因,在金属丰度研究的错误。来自HII区的强发射线经常被用来研究恒星形成区的金属丰度。我们比较集成的发射线光谱从我们的模型和量化任何系统误差所造成的简化假设的一个单一的,中心位置的所有电离源。我们发现,金属丰度指标对电离参数的依赖性会导致明显的偏差,这是由于这样一个事实,即具有完全分布的恒星配置的模型总是显示较低的电离参数比他们完全集中的同行。发现的错误意味着电离源的几何分布可能部分地解释了使用模型校准的经验方法得出的金属丰度的大分散。

英文摘要:

Spatially resolved studies of star forming regions show that the assumption of spherical geometry is not realistic in most cases, with a major complication posed by the gas being ionised by multiple non-centrally located stars or star clusters. We try to isolate the effects of multiple non-centrally located stars on the temperature and ionisation structure of HII regions, via the construction of 3D photoionisation models using the 3D Monte Carlo photoionisation code MOCASSIN. We find that the true temperature fluctuations due to the stellar distribution (as opposed to the large-scale temperature gradients due to other gas properties) are small in all cases and not a significant cause of error in metallicity studies. Strong emission lines from HII regions are often used to study the metallicity of star-forming regions. We compare integrated emission line spectra from our models and quantify any systematic errors caused by the simplifying assumption of a single, central location for all ionising sources. We find that the dependence of the metallicity indicators on the ionisation parameter causes a clear bias, due to the fact that models with a fully distributed configuration of stars always display lower ionisation parameters than their fully concentrated counterparts. The errors found imply that the geometrical distribution of ionisation sources may partly account for the large scatter in metallicities derived using model-calibrated empirical methods.

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