Statistical comparison of clouds and star clusters

TL;DR

This paper introduces a statistical method using the measure ${\

Contribution

It presents a new technique to compare the spatial distributions of gas and stars in molecular clouds and clusters using the ${\cal Q}$ parameter.

Findings

${\cal Q}$ effectively quantifies distribution structures.

The method can be applied to both star positions and grey-scale images.

Simulations demonstrate the technique's utility in astrophysical contexts.

Abstract

The extent to which the projected distribution of stars in a cluster is due to a large-scale radial gradient, and the extent to which it is due to fractal sub-structure, can be quantified -- statistically -- using the measure ${\cal Q} = \bar{m}/\bar{s}$. Here $\bar{m}$ is the normalized mean edge length of its minimum spanning tree (i.e. the shortest network of edges connecting all stars in the cluster) and $\bar{s}$ is the correlation length (i.e. the normalized mean separation between all pairs of stars). We show how ${\cal Q}$ can be indirectly applied to grey-scale images by decomposing the image into a distribution of points from which $\bar{m}$ and $\bar{s}$ can be calculated. This provides a powerful technique for comparing the distribution of dense gas in a molecular cloud with the distribution of the stars that condense out of it. We illustrate the application of this technique by comparing ${\cal Q}$ values from simulated clouds and star clusters.