The hierarchical structure of galactic haloes

• Verfasst von: Oliver, William H. [VerfasserIn]
• Verfasst von: Elahi, Pascal J. [VerfasserIn]
• Verfasst von: Lewis, Geraint F. [VerfasserIn]
• Verfasst von: Buck, Tobias [VerfasserIn]
• Titel: The hierarchical structure of galactic haloes
• Titelzusatz: differentiating clusters from stochastic clumping with astrolink
• Verf.angabe: William H. Oliver, Pascal J. Elahi, Geraint F. Lewis and Tobias Buck
• E-Jahr: 2024
• Jahr: May 2024
• Umfang: 11 S.
• Illustrationen: Illustrationen
• Fussnoten: Online veröffentlicht: 17. April 2024 ; Gesehen am 28.01.2025
• Titel Quelle: Enthalten in: Royal Astronomical SocietyMonthly notices of the Royal Astronomical Society
• Ort Quelle: Oxford : Oxford Univ. Press, 1827
• Jahr Quelle: 2024
• Band/Heft Quelle: 530(2024), 3 vom: Mai, Seite 2637-2647
• ISSN Quelle: 1365-2966
• DOI: doi:10.1093/mnras/stae1029
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1915764246

Abstract

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.