Identifying star clusters in a field: A comparison of different algorithms
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...
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| Main Author: | |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2011 Feb 15
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| In: |
Astronomical notes
Year: 2011, Volume: 332, Issue: 2, Pages: 172-184 |
| ISSN: | 0004-6337 |
| DOI: | 10.1002/asna.201011484 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1002/asna.201011484 Verlag, lizenzpflichtig, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/asna.201011484 
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| Author Notes: | S. Schmeja |
MARC
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| 520 | |a 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 nearest 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. | ||
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