Effects of Multichannel Active Galactic Nuclei Feedback in FIRE Cosmological Simulations of Massive Galaxies

Creators: Byrne, Lindsey¹; Faucher-Giguère, Claude-André¹; Wellons, Sarah²; Hopkins, Philip F.³; Anglés-Alcázar, Daniel^{4, 5}; Sultan, Imran¹; Wijers, Nastasha¹; Moreno, Jorge^{5, 6}; Ponnada, Sam³

1. Northwestern University
2. Wesleyan University
3. California Institute of Technology
4. University of Connecticut
5. Center for Computational Astrophysics, Flatiron Institute, 162 Fifth Avenue, New York, NY 10010, USA
6. Pomona College

Abstract

Abstract Feedback from supermassive black holes is believed to be a critical driver of the observed color bimodality of galaxies above the Milky Way mass scale. Active galactic nuclei (AGN) feedback has been modeled in many galaxy formation simulations, but most implementations have involved simplified prescriptions or a coarse-grained interstellar medium (ISM). We present the first set of Feedback In Realistic Environments (FIRE)-3 cosmological zoom-in simulations with AGN feedback evolved to z ∼ 0, examining the impact of AGN feedback on a set of galaxies with halos in the mass range 10¹²–10¹³ M⊙. These simulations combine detailed stellar and ISM physics with multichannel AGN feedback including radiative feedback, mechanical outflows, and, in some simulations, cosmic rays (CRs). We find that massive (>L*) galaxies in these simulations can match local scaling relations including the stellar mass–halo mass relation and the M BH–σ relation; in the stronger model with CRs, they also match the size–mass relation and the Faber–Jackson relation. Many of the massive galaxies in the simulations with AGN feedback have quenched star formation and elliptical morphologies, in qualitative agreement with observations. In contrast, simulations at the massive end without AGN feedback produce galaxies that are too massive and form stars too rapidly, are order-of-magnitude too compact, and have velocity dispersions well above Faber–Jackson. Despite these successes, the AGN models analyzed do not produce uniformly realistic galaxies when the feedback parameters are held constant: While the stronger model produces the most realistic massive galaxies, it tends to overquench the lower-mass galaxies. This indicates that further refinements of the AGN modeling are needed.

Copyright and License (English)

© 2024. The Author(s). Published by the American Astronomical Society. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

Acknowledgement (English)

We thank the referee for comments that helped us clarify this paper. L.B. was supported by the DOE Computer Science Graduate Fellowship through grant No. DE-SC0020347. C.A.F.G. was supported by the NSF through grants AST-2108230, AST-2307327, and CAREER award AST-1652522; by NASA through grant Nos. 17-ATP17-0067 and 21-ATP21-0036; by STScI through grant No. HST-GO-16730.016-A; and by CXO through grant No. TM2-23005X. Support for P.F.H. and S.P. was provided by NSF Research grants 1911233, 20009234, 2108318, NSF CAREER grant 1455342, and NASA grant Nos. 80NSSC18K0562 and HST-AR-15800. D.A.A. acknowledges support by NSF grants AST-2009687 and AST-2108944, CXO grant No. TM2-23006X, JWST grant Nos. GO-01712.009-A and AR-04357.001-A, Simons Foundation Award CCA-1018464, and Cottrell Scholar Award CS-CSA-2023-028 by the Research Corporation for Science Advancement. J.M. is funded by the Hirsch Foundation. N.A.W. was supported by a CIERA Postdoctoral Fellowship. Numerical calculations were run on the Caltech computer cluster Wheeler; the Northwestern computer cluster Quest; Frontera allocation FTA-Hopkins/AST20016 supported by the NSF and TACC; XSEDE allocations ACI-1548562, TGAST140023, and TG-AST140064; and NASA HEC allocations SMD-16-7561, SMD-17-1204, and SMD-16-7592. Some figures were generated with the help of FIRE Studio, an open-source Python visualization package (Gurvich 2022).

Funding (English)

L.B. was supported by the DOE Computer Science Graduate Fellowship through grant No. DE-SC0020347. C.A.F.G. was supported by the NSF through grants AST-2108230, AST-2307327, and CAREER award AST-1652522; by NASA through grant Nos. 17-ATP17-0067 and 21-ATP21-0036; by STScI through grant No. HST-GO-16730.016-A; and by CXO through grant No. TM2-23005X. Support for P.F.H. and S.P. was provided by NSF Research grants 1911233, 20009234, 2108318, NSF CAREER grant 1455342, and NASA grant Nos. 80NSSC18K0562 and HST-AR-15800. D.A.A. acknowledges support by NSF grants AST-2009687 and AST-2108944, CXO grant No. TM2-23006X, JWST grant Nos. GO-01712.009-A and AR-04357.001-A, Simons Foundation Award CCA-1018464, and Cottrell Scholar Award CS-CSA-2023-028 by the Research Corporation for Science Advancement. J.M. is funded by the Hirsch Foundation. N.A.W. was supported by a CIERA Postdoctoral Fellowship. Numerical calculations were run on the Caltech computer cluster Wheeler; the Northwestern computer cluster Quest; Frontera allocation FTA-Hopkins/AST20016 supported by the NSF and TACC; XSEDE allocations ACI-1548562, TGAST140023, and TG-AST140064; and NASA HEC allocations SMD-16-7561, SMD-17-1204, and SMD-16-7592.

Data Availability (English)

The data supporting the plots within this article are available on reasonable request to the corresponding author. Additional data including simulation snapshots, initial conditions, and derived data products are available at http://fire.northwestern.edu/data/.

Code Availability (English)

A public version of the GIZMO code is available at http://www.tapir.caltech.edu/~phopkins/Site/GIZMO.html.

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