Cloud properties across spatial scales in simulations of the interstellar medium

Context. Molecular clouds (MCs) are structures of dense gas in the interstellar medium (ISM) that extend from ten to a few hundred parsecs and form the main gas reservoir available for star formation. Hydrodynamical simulations of a varying complexity are a promising way to investigate MCs evolution and their properties. However, each simulation typically has a limited range in resolution and different cloud extraction algorithms are used, which complicates the comparison between simulations.
Aims: In this work, we aim to extract clouds from different simulations covering a wide range of spatial scales. We compare their properties, such as size, shape, mass, internal velocity dispersion, and virial state.
Methods: We applied the HOP cloud detection algorithm on (M)HD numerical simulations of stratified ISM boxes and isolated galactic disk simulations that were produced using FLASH, RAMSES, and AREPO.
Results: We find that the extracted clouds are complex in shape, ranging from round objects to complex filamentary networks in all setups. Despite the wide range of scales, resolution, and sub-grid physics, we observe surprisingly robust trends in the investigated metrics. The mass spectrum matches in the overlap between simulations without rescaling and with a high-mass power-law index of −1 for logarithmic bins of mass, in accordance with theoretical predictions. The internal velocity dispersion scales with the size of the cloud as σ ∝ R^0.75 for large clouds (R ≳ 3 pc). For small clouds we find larger σ compared to the power-law scaling, as seen in observations, which is due to supernova-driven turbulence. Almost all clouds are gravitationally unbound with the virial parameter scaling as α_vir ∝ M⁻⁰⁴, which is slightly flatter compared to observed scaling but in agreement given the large scatter. We note that the cloud distribution towards the low-mass end is only complete if the more dilute gas is also refined, rather than only the collapsing regions.