Grouped star formation: converting sink particles to stars in hydrodynamical simulations

Abstract

Modelling star formation and resolving individual stars in numerical simulations of molecular clouds and galaxies is highly challenging. Simulations on very small scales can be sufficiently well resolved to consistently follow the formation of individual stars, whilst on larger scales sinks that have masses sufficient to fully sample the IMF can be converted into realistic stellar populations. However, as yet, these methods do not work for intermediate scale resolutions whereby sinks are more massive compared to individual stars but do not fully sample the IMF. In this paper, we introduce the grouped star formation prescription, whereby sinks are first grouped according to their positions, velocities, and ages, then stars are formed by sampling the IMF using the mass of the groups. We test our grouped star formation method in simulations of various physical scales, from sub-parsec to kilo-parsec, and from static isolated clouds to colliding clouds. With suitable grouping parameters, this star formation prescription can form stars that follow the IMF and approximately mimic the original stellar distribution and velocity dispersion. Each group has properties that are consistent with a star-forming region. We show that our grouped star formation prescription is robust and can be adapted in simulations with varying physical scales and resolution. Such methods are likely to become more important as galactic or even cosmological scale simulations begin to probe sub-parsec scales.

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

“…In Dobbs et al (2020) we argue that cloud-cloud collisions may be one way of producing massive clusters in short timescales, and are in any case more representative of conditions in more extreme environments in our or other galaxies. To check our results are representative, and not dependent on this one model we have chosen, we also perform analysis on the low speed, medium density cloud-cloud collision model, labelled as 'L20Turbulent' in Liow et al (2022), which we describe in Appendix A. This model is a rerun of the model of the same initial conditions in , i.e.…”

“…Stars are positioned randomly within the accretion radius of the sink, and with a velocity dispersion similar to the surrounding gas velocity dispersion. We use the grouped star formation in Ekster (Liow et al 2022). This is a star formation prescription which first collects the sinks into multiple groups.…”

“…To check whether our findings for the effects of 2D perspective on cluster characterisation are not unique to our chosen cluster, we repeat our analysis on a second simulation, which is also the 'L20Turbulent' model in Liow et al (2022).…”

“…the initial cloud density is 1.03 × 10 −21 g cm −3 and the relative velocity is 10 km s −1 . The parameters are mostly similar to those used in the main simulation in this paper, however the details can be found in Section 2.3.2 of Liow et al (2022). In this model as shown in Figure A1, stars are formed via collision at the compressed central region.…”

The formation of clusters and OB associations in different density spiral arm environments

“…To fully model clusters, one possibility for cosmological or galactic simulations is to include the cluster evolution separately using a theoretical or empirical model (Pfeffer et al 2018) or by separately evolving a sample of clusters (Grudić et al 2022). An alternative approach is to model the full population of stars with Nbody dynamics (Fujii et al 2021;Rieder et al 2022;Liow et al 2022), even if the gas resolution is low, thus achieving the same resolution in the stars as individual cluster simulations. This has the advantage that the cluster dynamics can be followed explicitly.…”