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Published April 21, 2016 | Submitted + Published
Journal Article Open

Dust formation in Milky Way-like galaxies

Abstract

We introduce a dust model for cosmological simulations implemented in the moving-mesh code AREPO and present a suite of cosmological hydrodynamical zoom-in simulations to study dust formation within galactic haloes. Our model accounts for the stellar production of dust, accretion of gas-phase metals on to existing grains, destruction of dust through local supernova activity, and dust driven by winds from star-forming regions. We find that accurate stellar and active galactic nuclei feedback is needed to reproduce the observed dust–metallicity relation and that dust growth largely dominates dust destruction. Our simulations predict a dust content of the interstellar medium which is consistent with observed scaling relations at z = 0, including scalings between dust-to-gas ratio and metallicity, dust mass and gas mass, dust-to-gas ratio and stellar mass, and dust-to-stellar mass ratio and gas fraction. We find that roughly two-thirds of dust at z = 0 originated from Type II supernovae, with the contribution from asymptotic giant branch stars below 20 per cent for z ≳ 5. While our suite of Milky Way-sized galaxies forms dust in good agreement with a number of key observables, it predicts a high dust-to-metal ratio in the circumgalactic medium, which motivates a more realistic treatment of thermal sputtering of grains and dust cooling channels.

Additional Information

© 2016 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. Accepted 2016 January 28. Received 2016 January 28; in original form 2015 May 18. First published online February 2, 2016. We thank Lia Corrales, Christopher Hayward, Alexander Ji, Rahul Kannan, Federico Marinacci, Diego Muñoz, and Gregory Snyder for helpful discussion and Volker Springel for making AREPO available. We are also grateful to the anonymous referee for comments that improved this manuscript. The simulations were performed on the joint MIT-Harvard computing cluster supported by MKI and FAS. RM acknowledges support from the DOE CSGF under grant number DE-FG02- 97ER25308. MV acknowledges support through an MIT RSC award.

Attached Files

Published - MNRAS-2016-McKinnon-3775-800.pdf

Submitted - 1505.04792v2.pdf

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