The formation and early evolution of embedded star clusters in spiral galaxies

Rieder, Steven; Dobbs, Clare; Bending, Thomas; Liow, Kong You; Wurster, James

doi:10.1093/mnras/stab3425

Cited by 21 publications

(14 citation statements)

References 104 publications

(110 reference statements)

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“…In this previous paper we ran this simulation using PHANTOM (Price et al 2018). However since this publication, we have developed the Ekster code (Rieder et al 2022). The Ekster code converts sink particles to star particles, sampling from an IMF, which in contrast to most numerical simulations allows us to model the full stellar population of the cluster.…”

Section: Simulation Setupmentioning

confidence: 99%

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

Buckner,

Liow,

Dobbs

et al. 2022

Preprint

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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.

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Section: Simulation Setupmentioning

confidence: 99%

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

Buckner,

Liow,

Dobbs

et al. 2022

Preprint

Self Cite

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“…The systems then continue to evolve together, with the cluster perceiving the potential generated by the environment. We treated this interaction using the Bridge routine implemented in AMUSE, and letting the code Fi (Gerritsen & Icke 1997;Pelupessy et al 2004) generate the potential field of the gas (Rieder et al 2022). We note that the domain of the gaseous environment is periodic.…”

Section: Cluster and Environmentmentioning

confidence: 99%

Environmental effects on the dynamical evolution of star clusters in turbulent molecular clouds

Suin

Shore

Pavlík

2022

A&A

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Context. Star clusters form within giant molecular clouds that are strongly altered by the feedback action of the massive stars, but the cluster still remains embedded in a dense, highly turbulent medium and interactions with ambient structures may modify its dynamical evolution from that expected if it were isolated. Aims. We aim to study coupling mechanisms between the dynamical evolution of the cluster, accelerated by the mass segregation process, with harassment effects caused by the gaseous environment. Methods. We simulated the cluster dynamical evolution combining N-body and hydrodynamic codes within the Astronomical Multipurpose Software Environment (AMUSE). Results. Tidal harassment produces a sparser configuration more rapidly than the isolated reference simulations. The evolution of the asymptotic power-law density distribution exponent also shows substantially different behaviour in the two cases. The background is more effective on clusters in advanced stages of dynamical development.

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“…We use Ekster via AMUSE (Rieder & Liow 2021;Rieder et al 2022), a multiphysical code that combines gas hydrodynamics using Phantom (Price et al 2018), high-performance gravitational dynamics using PeTar (Wang et al 2020), and stellar evolution using SeBa (Portegies Zwart & Verbunt 1996). The gravitational dynamics between gas and non-gas is coupled using Bridge (Fujii et al 2007).…”

Section: Simulationmentioning

confidence: 99%