论文标题

HII区域的形状分析-II。合成观察

Shape Analysis of HII Regions -- II. Synthetic Observations

论文作者

Campbell-White, Justyn, Ali, Ahmad A., Froebrich, Dirk, Kume, Alfred

论文摘要

此处将用于探测物理参数和银河系HII区域形态之间的联系开发的统计形状分析方法,此处应用于数字建模的HII区域的一组合成观测值(SOS)。在本系列的第一篇论文中提出的HII区域形状的系统提取允许可量化数值模拟的准确性,相对于所得SOS的实际观测值对应物。这项研究的另一个目的是确定在基于HII区域形状的未来监督分类方案中,是否可以将这种SO用于直接解释观测数据。数值HII区域数据是在1000 MSUN云中的34 MSUN Star的光电子化和辐射压力反馈的结果。本文分析的SOS包括四个进化快照(0.1、0.2、0.4和0.6 MYR)和多个观看投影角。形状分析结果提供了数值模拟功效的确定证据。当将合成区域的形状与观察性对应物进行比较时,通过分层聚类程序将SOS分组在银河HII区域中。区域的进化分布与各个组之间也存在关联。这表明,可以通过使用合成数据训练集(具有不同定义明确的参数的初始条件,可以进一步开发形状分析方法来对HII区域进行形态学分类。

The statistical shape analysis method developed for probing the link between physical parameters and morphologies of Galactic HII regions is applied here to a set of synthetic observations (SOs) of a numerically modelled HII region. The systematic extraction of HII region shape, presented in the first paper of this series, allows for a quantifiable confirmation of the accuracy of the numerical simulation, with respect to the real observational counterparts of the resulting SOs. A further aim of this investigation is to determine whether such SOs can be used for direct interpretation of the observational data, in a future supervised classification scheme based upon HII region shape. The numerical HII region data was the result of photoionisation and radiation pressure feedback of a 34 Msun star, in a 1000 Msun cloud. The SOs analysed herein comprised four evolutionary snapshots (0.1, 0.2, 0.4 and 0.6 Myr), and multiple viewing projection angles. The shape analysis results provided conclusive evidence of the efficacy of the numerical simulations. When comparing the shapes of the synthetic regions to their observational counterparts, the SOs were grouped in amongst the Galactic HII regions by the hierarchical clustering procedure. There was also an association between the evolutionary distribution of regions and the respective groups. This suggested that the shape analysis method could be further developed for morphological classification of HII regions by using a synthetic data training set, with differing initial conditions of well-defined parameters.

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