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Published December 2017 | Published
Journal Article Open

Comparing models for IMF variation across cosmological time in Milky Way-like galaxies

Abstract

One of the key observations regarding the stellar initial mass function (IMF) is its near-universality in the Milky Way (MW), which provides a powerful way to constrain different star formation models that predict the IMF. However, those models are almost universally 'cloud-scale' or smaller – they take as input or simulate single molecular clouds (GMCs), clumps or cores, and predict the resulting IMF as a function of the cloud properties. Without a model for the progenitor properties of all clouds that formed the stars at different locations in the MW (including ancient stellar populations formed in high redshift, likely gas-rich dwarf progenitor galaxies that looked little like the Galaxy today), the predictions cannot be fully explored nor safely applied to 'live' cosmological calculations of the IMF in different galaxies at different cosmological times. We therefore combine a suite of high-resolution cosmological simulations (from the Feedback In Realistic Environments project), which form MW-like galaxies with reasonable star formation properties and explicitly resolve massive GMCs, with various proposed cloud-scale IMF models. We apply the models independently to every star particle formed in the simulations to synthesize the predicted IMF in the present-day galaxy. We explore models where the IMF depends on Jeans mass, sonic or 'turbulent Bonnor–Ebert' mass, fragmentation with a polytropic equation of state, or where it is self-regulated by protostellar feedback. We show that all of these models, except the feedback-regulated ones, predict far more variation (∼0.6–1 dex 1σ scatter in the IMF turnover mass) in the simulations than is observed in the MW.

Additional Information

© 2017 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society. Accepted 2017 August 8. Received 2017 July 17; in original form 2017 February 14. Published: 14 August 2017. Support for PFH, DG and XM was provided by Alfred P. Sloan Research Fellowship, NASA ATP Grant NNX14AH35G, and NSF Collaborative Research Grant #1411920 and CAREER grant #1455342. Numerical calculations were run on the Caltech compute cluster 'Zwicky' (NSF MRI award #PHY-0960291) and allocation TG-AST130039 granted by the Extreme Science and Engineering Discovery Environment (XSEDE) supported by the NSF.

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August 19, 2023
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