Shape Analysis of HII Regions -- II. Synthetic Observations

TL;DR

This paper applies a statistical shape analysis to synthetic observations of modeled HII regions to validate simulations and explore shape-based classification of real Galactic HII regions.

Contribution

It demonstrates the effectiveness of shape analysis in confirming simulation accuracy and proposes its use for morphological classification of HII regions using synthetic data.

Findings

Synthetic HII region shapes match observational counterparts.

Hierarchical clustering groups synthetic regions with real Galactic HII regions.

Shape analysis correlates with evolutionary stages of HII regions.

Abstract

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.