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Published March 2021 | Submitted + Published
Journal Article Open

Effects of different cosmic ray transport models on galaxy formation

Abstract

Cosmic rays (CRs) with ∼GeV energies can contribute significantly to the energy and pressure budget in the interstellar, circumgalactic, and intergalactic medium (ISM, CGM, IGM). Recent cosmological simulations have begun to explore these effects, but almost all studies have been restricted to simplified models with constant CR diffusivity and/or streaming speeds. Physical models of CR propagation/scattering via extrinsic turbulence and self-excited waves predict transport coefficients which are complicated functions of local plasma properties. In a companion paper, we consider a wide range of observational constraints to identify proposed physically motivated cosmic ray propagation scalings which satisfy both detailed Milky Way (MW) and extragalactic γ-ray constraints. Here, we compare the effects of these models relative to simpler 'diffusion+streaming' models on galaxy and CGM properties at dwarf through MW mass scales. The physical models predict large local variations in CR diffusivity, with median diffusivity increasing with galactocentric radii and decreasing with galaxy mass and redshift. These effects lead to a more rapid dropoff of CR energy density in the CGM (compared to simpler models), in turn producing weaker effects of CRs on galaxy star formation rates (SFRs), CGM absorption profiles, and galactic outflows. The predictions of the more physical CR models tend to lie 'in between' models which ignore CRs entirely and models which treat CRs with constant diffusivity.

Additional Information

© 2020 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 2020 November 15. Received 2020 November 10; in original form 2020 March 22. Published: 28 November 2020. We thank the anonymous referee for a number of insightful comments. Support for PFH was provided by NSF Collaborative Research Grants 1715847 & 1911233, NSF CAREER grant 1455342, and NASA grants 80NSSC18K0562 and JPL 1589742. CAFG was supported by NSF 1517491, 1715216, and CAREER 1652522; NASA 17-ATP17-0067; and by a Cottrell Scholar Award. Support for JS was provided by Rutherford Discovery Fellowship RDF-U001804 and Marsden Fund grant UOO1727 from the Royal Society Te Apārangi. DK was supported by NSF grant AST-1715101 and the Cottrell Scholar Award from the Research Corporation for Science Advancement. Numerical calculations were run on the Caltech compute cluster 'Wheeler,' allocations FTA-Hopkins supported by the NSF and TACC, and NASA HEC SMD-16-7592. Data Availability Statement: 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. Additional data including simulation snapshots, initial conditions, and derived data products are available at http://fire.northwestern.edu.

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Published - staa3692.pdf

Submitted - 2004.02897.pdf

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Additional details

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