SIMBAD references

2019MNRAS.485.4852G - Mon. Not. R. Astron. Soc., 485, 4852-4862 (2019/June-1)

Is it possible to reconcile extragalactic IMF variations with a universal Milky Way IMF?


Abstract (from CDS):

One of the most robust observations of the stellar initial mass function (IMF) is its near-universality in the Milky Way and neighbouring galaxies. But recent observations of early-type galaxies can be interpreted to imply a 'bottom-heavy' IMF, while others of ultrafaint dwarfs could imply a 'top-heavy' IMF. This would impose powerful constraints on star formation models. We explore what sort of 'cloud-scale' IMF models could possibly satisfy these constraints. We utilize simulated galaxies that reproduce (broadly) the observed galaxy properties, while they also provide the detailed star formation history and properties of each progenitor star-forming cloud. We then consider generic models where the characteristic mass of the IMF is some arbitrary power-law function of progenitor cloud properties, along with well-known literature IMF models which scale with Jeans mass, 'turbulent Bonnor-Ebert mass', temperature, the opacity limit, metallicity, or the 'protostellar heating mass'. We show that no IMF models currently in the literature - nor any model where the turnover mass is an arbitrary power-law function of a combination of cloud temperature/density/size/metallicity/velocity dispersion/magnetic field - can reproduce the claimed IMF variation in ellipticals or dwarfs without severely violating observational constraints in the Milky Way. Specifically, they predict too much variation in the 'extreme' environments of the Galaxy compared to that observed. Either the IMF varies in a more complicated manner, or alternative interpretations of the extragalactic observations must be explored.

Abstract Copyright: © 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society

Journal keyword(s): turbulence - stars: formation - galaxies: star formation - cosmology: theory

Simbad objects: 4

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