2018
DOI: 10.1093/mnras/sty2372
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Evolution of spatio-kinematic structures in star-forming regions: are Friends of Friends worth knowing?

Abstract: The Friends of Friends algorithm identifies groups of objects with similar spatial and kinematic properties, and has recently been used extensively to quantify the distributions of gas and stars in young star-forming regions. We apply the Friends of Friends algorithm to N-body simulations of the dynamical evolution of subvirial (collapsing) and supervirial (expanding) star-forming regions. We find that the algorithm picks out a wide range of groups (1-25) for statistically identical initial conditions, and can… Show more

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Cited by 8 publications
(6 citation statements)
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“…When we do this simplistic tallying, we find that the simulations most consistent with the observations are those that start with compact (r F = 1 pc) and substructured (D = 1.6) initial conditions, which are very dense (10 4 M ⊙ pc −3 ). The initial bulk motion can either be subvirial (α vir = 0.3) or in virial equilibrium (α vir = 0.5); in fact, this is a weak constraint because the local velocity dispersions in the substructure are usually highly subvirial (Parker & Wright 2016;Parker & Wright 2018). These compact, highly substructured initial conditions are also the optimal initial conditions identified from the RW/WW analysis in Schoettler et al (2021).…”
Section: N-body Simulationsmentioning
confidence: 99%
“…When we do this simplistic tallying, we find that the simulations most consistent with the observations are those that start with compact (r F = 1 pc) and substructured (D = 1.6) initial conditions, which are very dense (10 4 M ⊙ pc −3 ). The initial bulk motion can either be subvirial (α vir = 0.3) or in virial equilibrium (α vir = 0.5); in fact, this is a weak constraint because the local velocity dispersions in the substructure are usually highly subvirial (Parker & Wright 2016;Parker & Wright 2018). These compact, highly substructured initial conditions are also the optimal initial conditions identified from the RW/WW analysis in Schoettler et al (2021).…”
Section: N-body Simulationsmentioning
confidence: 99%
“…Spatial analyses of observations and simulations have been applied to density and radius estimation, membership, and multiplicity determination, as well as mass segregation (see e.g., Casertano & Hut 1985;Gomez et al 1993;Larson 1995;Maíz-Apellániz et al 2004;Cartwright & Whitworth 2004;Allison et al 2009;Parker & Goodwin 2015;Maschberger & Clarke 2011;Buckner et al 2019). In recent years, analyses have been extended to the spatio-kinematical phase space, allowing us to estimate the kinematical state of clusters and associations, generate catalogues, or distinguish between different populations within the Milky Way (see e.g., Fűrész et al 2006;Parker et al 2014;Wright et al 2014;Alfaro & González 2016;Parker & Wright 2018;Cantat-Gaudin et al 2018).…”
Section: Introductionmentioning
confidence: 99%
“…When we do this simplistic tallying, we find that the simulations most consistent with the observations are those that start with compact (r F = 1 pc) and substructured (D = 1.6) initial conditions, which are very dense (10 4 M ⊙ pc −3 ). The initial bulk motion can either be subvirial (α vir = 0.3) or in virial equilibrium (α vir = 0.5); in fact, this is a weak constraint because the local velocity dispersions in the substructure are usually highly subvirial (Parker & Wright 2016;Parker & Wright 2018). These compact, highly substructured initial conditions are also the optimal initial conditions identified from the RW/WW analysis in Schoettler et al (2021).…”
Section: N-body Simulationsmentioning
confidence: 99%