Observational Bias and Young Massive Cluster Characterisation I. 2D Perspective Effects

TL;DR

This study investigates how observational perspective affects the measurement of properties in young massive star clusters, revealing significant inaccuracies in 2D data interpretations, especially regarding kinematics and subcluster identification.

Contribution

It introduces a method to compare 3D simulated cluster parameters with those derived from 2D projections, highlighting the limitations of 2D observational data for cluster analysis.

Findings

2D perspectives can qualitatively match 3D conclusions but are quantitatively unreliable.

Perceived cluster expansion or contraction can be misleading depending on line-of-sight.

Subcluster identification accuracy decreases with cluster evolution, dropping below 3.4%.

Abstract

Understanding the formation and evolution of high mass star clusters requires comparisons between theoretical and observational data to be made. Unfortunately, while the full phase space of simulated regions is available, often only partial 2D spatial and kinematic data is available for observed regions. This raises the question as to whether cluster parameters determined from 2D data alone are reliable and representative of clusters real parameters and the impact of line-of-sight orientation. In this paper we derive parameters for a simulated cluster formed from a cloud-cloud collision with the full 6D phase space, and compare them with those derived from three different 2D line-of-sight orientations for the cluster. We show the same qualitative conclusions can be reached when viewing clusters in 2D versus 3D, but that drawing quantitative conclusions when viewing in 2D is likely to be inaccurate. The greatest divergence occurs in the perceived kinematics of the cluster, which in some orientations appears to be expanding when the cluster is actually contracting. Increases in the cluster density compounds pre-existing perspective issues, reducing the relative accuracy and consistency of properties derived from different orientations. This is particularly problematic for determination of the number, and membership, of subclusters present in the cluster. We find the fraction of subclusters correctly identified in 2D decreases as the cluster evolves, reaching less than 3.4% at the evolutionary end point for our cluster.