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Published August 2022 | Submitted + Published
Journal Article Open

The galactic dust-up: modelling dust evolution in FIRE

Abstract

Recent strides have been made developing dust evolution models for galaxy formation simulations but these approaches vary in their assumptions and degree of complexity. Here, we introduce and compare two separate dust evolution models (labelled 'Elemental' and 'Species'), based on recent approaches, incorporated into the GIZMO code and coupled with FIRE-2 stellar feedback and interstellar medium physics. Both models account for turbulent dust diffusion, stellar production of dust, dust growth via gas-dust accretion, and dust destruction from time-resolved supernovae, thermal sputtering in hot gas, and astration. The 'Elemental' model tracks the evolution of generalized dust species and utilizes a simple, 'tunable' dust growth routine, while the 'Species' model tracks the evolution of specific dust species with set chemical compositions and incorporates a physically motivated, two-phase dust growth routine. We test and compare these models in an idealized Milky Way-mass galaxy and find that while both produce reasonable galaxy-integrated dust-to-metals (D/Z) ratios and predict gas-dust accretion as the main dust growth mechanism, a chemically motivated model is needed to reproduce the observed scaling relation between individual element depletions and D/Z with column density and local gas density. We also find the inclusion of theoretical metallic iron and O-bearing dust species are needed in the case of specific dust species in order to match observations of O and Fe depletions, and the integration of a sub-resolution dense molecular gas/CO scheme is needed to both match observed C depletions and ensure carbonaceous dust is not overproduced in dense environments.

Additional Information

© 2022 The Author(s). Published by Oxford University Press on behalf of Royal Astronomical Society. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model). Accepted 2022 May 23. Received 2022 May 17; in original form 2022 January 26. Published: 07 June 2022. We thank I-Da Chiang, Lichen Liang, and Alexander J. Richings for insightful suggestions and advice. CC and DK were supported by NSF grants AST-1715101 and AST2108314 and the Cottrell Scholar Award from the Research Corporation for Science Advancement. Numerical calculations were run on the UC San Diego Triton Shared Computing Cluster, and allocations AST20016 and TG-AST140023 supported by the NSF and TACC. Support for PFH was provided by NSF Research Grants 1911233 and 20009234, NSF CAREER grant 1455342, NASA grants 80NSSC18K0562, HST-AR-15800.001-A. KS was supported by NSF grant No. 1615728. CAFG was supported by NSF through grants AST-1715216, AST-2108230, and NSF CAREER award AST-1652522; by NASA through grant 17-ATP17-0067; by STScI through grant HST-AR-16124.001-A; and by the Research Corporation for Science Advancement through a Cottrell Scholar Award. The data used in this work were, in part, hosted on facilities supported by the Scientific Computing Core at the Flatiron Institute, a division of the Simons Foundation. This work also made use of MATPLOTLIB (Hunter 2007), NUMPY (Harris et al. 2020), SCIPY (Virtanen et al. 2020), the yt project (Turk et al. 2011), and NASA's Astrophysics Data System. Data Availability: The data supporting the plots within this article are available on reasonable request to the corresponding author. A public version of the GIZMO code is available at http://www.tapir.caltech.edu/~phopkins/Site/GIZMO.html.

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Submitted - 2201.12369.pdf

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Additional details

Created:
August 20, 2023
Modified:
October 23, 2023