The hierarchical structure of galactic haloes: differentiating clusters from stochastic clumping with astrolink
We present astrolink, an efficient and versatile clustering algorithm designed to hierarchically classify astrophysically relevant structures from both synthetic and observational data sets. We build upon clustar-nd, a hierarchical galaxy/(sub)halo finder, so that astrolink now generates a 2D repres...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
May 2024
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| In: |
Monthly notices of the Royal Astronomical Society
Year: 2024, Volume: 530, Issue: 3, Pages: 2637-2647 |
| ISSN: | 1365-2966 |
| DOI: | 10.1093/mnras/stae1029 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1093/mnras/stae1029
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| Author Notes: | William H. Oliver, Pascal J. Elahi, Geraint F. Lewis and Tobias Buck |
| Summary: | We present astrolink, an efficient and versatile clustering algorithm designed to hierarchically classify astrophysically relevant structures from both synthetic and observational data sets. We build upon clustar-nd, a hierarchical galaxy/(sub)halo finder, so that astrolink now generates a 2D representation of the implicit clustering structure as well as ensuring that clusters are statistically distinct from the noisy density fluctuations implicit within the n-dimensional input data. This redesign replaces the three cluster extraction parameters from clustar-nd with a single parameter, S – the lower statistical significance threshold of clusters, which can be automatically and reliably estimated via a dynamical model-fitting process. We demonstrate the robustness of this approach compared to astrolink’s predecessors by applying each algorithm to a suite of simulated galaxies defined over various feature spaces. We find that astrolink delivers a more powerful clustering performance while being ∼ 27 per cent faster and using less memory than clustar-nd. With these improvements, astrolink is ideally suited to extracting a meaningful set of hierarchical and arbitrarily shaped astrophysical clusters from both synthetic and observational data sets – lending itself as a great tool for morphological decomposition within the context of hierarchical structure formation. |
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| Item Description: | Online veröffentlicht: 17. April 2024 Gesehen am 28.01.2025 |
| Physical Description: | Online Resource |
| ISSN: | 1365-2966 |
| DOI: | 10.1093/mnras/stae1029 |