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

TL;DR

This paper introduces a grouped star formation method for hydrodynamical simulations that effectively models star formation at intermediate resolutions by grouping sinks and sampling the IMF, enabling realistic stellar populations across various scales.

Contribution

The paper presents a novel grouped star formation prescription that improves star formation modeling in simulations with intermediate resolution, bridging the gap between small-scale and large-scale methods.

Findings

The method accurately reproduces the IMF and stellar distributions.

It is robust across different physical scales and resolutions.

Groups resemble real star-forming regions.

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.