Identifying star clusters in a field: A comparison of different algorithms

TL;DR

This paper compares four algorithms for identifying star clusters in dense fields, evaluating their effectiveness and computational efficiency across various cluster types and morphologies.

Contribution

It provides a systematic comparison of four cluster detection algorithms, highlighting their strengths, limitations, and suitability for different cluster characteristics.

Findings

All methods detect centrally concentrated clusters.

Methods with smoothing detect low-overdensity or hierarchical clusters.

Algorithms differ significantly in computation time and parameters.

Abstract

Star clusters are often hard to find, as they may lie in a dense field of background objects or, because in the case of embedded clusters, they are surrounded by a more dispersed population of young stars. This paper discusses four algorithms that have been developed to identify clusters as stellar density enhancements in a field, namely stellar density maps from star counts, the neareast neighbour method and the Voronoi tessellation, and the separation of minimum spanning trees. These methods are tested and compared to each other by applying them to artificial clusters of different sizes and morphologies. While distinct centrally concentrated clusters are detected by all methods, clusters with low overdensity or highly hierarchical structure are only reliably detected by methods with inherent smoothing (star counts and nearest neighbour method). Furthermore, the algorithms differ strongly in computation time and additional parameters they provide. Therefore, the method to choose primarily depends on the size and character of the investigated area and the purpose of the study.