Comparing the properties of GMCs in M33 from simulations and observations

Abstract

We compare the properties of clouds in simulated M33 galaxies to those observed in the real M33. We apply a friends of friends algorithm and CPROPS to identify clouds, as well as a pixel by pixel analysis. We obtain very good agreement between the number of clouds, and maximum mass of clouds. Both are lower than occurs for a Milky Way-type galaxy and thus are a function of the surface density, size and galactic potential of M33. We reproduce the observed dependence of molecular cloud properties on radius in the simulations, and find this is due to the variation in gas surface density with radius. The cloud spectra also show good agreement between the simulations and observations, but the exact slope and shape of the spectra depends on the algorithm used to find clouds, and the range of cloud masses included when fitting the slope. Properties such as cloud angular momentum, velocity dispersions and virial relation are also in good agreement between the simulations and observations, but do not necessarily distinguish between simulations of M33 and other galaxy simulations. Our results are not strongly dependent on the level of feedback used here (10 and 20%) although they suggest that 15% feedback efficiency may be optimal. Overall our results suggest that the molecular cloud properties are primarily dependent on the gas and mass surface density, and less dependent on the localised physics such as the details of stellar feedback, or the numerical code used.

Molecular Gas Properties on Cloud Scales across the Local Star-forming Galaxy Population

“…Following our analysis of the PHANGS-ALMA pilot sample of 11 galaxies (Sun et al 2018, hereafter S18), we derive these measurements on fixed 90 pc and 150 pc scales using the full PHANGS-ALMA survey, which increases our sample size to 70 galaxies. The derived measurements constitute a benchmark data set that can be readily compared with observations of other types of galaxies or numerical simulations reaching similar spatial resolutions (e.g., Semenov et al 2018;Dobbs et al 2019;Fujimoto et al 2019;Jeffreson et al 2020).…”

Spatial power spectra of dust across the Local Group: No constraint on disc scale height

“…Even if multiple spatial distributions yield the same power spectrum index, our results still a key benchmark for simulations that aim to reproduce Local Group-like galaxies. Several recent works aim to simulate galaxies with properties closely matching the LMC, SMC, M31, M33, or the Milky Way (Combes et al 2012;Wetzel et al 2016;Grisdale et al 2017;Dobbs et al 2018;Garrison-Kimmel et al 2019) with many producing "synthetic" observations to compare with properties found in the actual observations (e.g., Dobbs et al 2019), a key step for directly comparing simulations and observations (Haworth et al 2018). For any simulation the produces dust maps or synthetic IR observations, matching our measured power spectrum represents an important check.…”

Evolution of giant molecular clouds across cosmic time

“…These methods require the value of the density cut, surface density cut, or linking length as an input parameter. They have been used in previous studies of galaxy simulations, and it has generally been found that for an appropriate choice of these parameters, one recovers cloud properties that are in good agreement with observations in the local Universe (Dobbs et al 2011;Hopkins et al 2012;Dobbs & Pringle 2013;Ibáñez-Mejía et al 2017;Hopkins et al 2018;Dobbs et al 2019;Fujimoto et al 2019). However, a cloud definition that is valid for the relatively narrow range of ISM conditions found in nearby galaxies where most GMCs are catalogued (Bolatto et al 2008) may not generalize well to high-redshift conditions.…”