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A mass-dependent density profile for dark matter haloes including the influence of galaxy formation
Open accessAbstract
We introduce a mass-dependent density profile to describe the distribution of dark matter within galaxies, which takes into account the stellar-to-halo mass dependence of the response of dark matter to baryonic processes. The study is based on the analysis of hydrodynamically simulated galaxies from dwarf to Milky Way mass, drawn from the Making Galaxies In a Cosmological Context project, which have been shown to match a wide range of disc scaling relationships. We find that the best-fitting parameters of a generic double power-law density profile vary in a systematic manner that depends on the stellar-to-halo mass ratio of each galaxy. Thus, the quantity M⋆/Mhalo constrains the inner (γ) and outer (β) slopes of dark matter density, and the sharpness of transition between the slopes (α), reducing the number of free parameters of the model to two. Due to the tight relation between stellar mass and halo mass, either of these quantities is sufficient to describe the dark matter halo profile including the effects of baryons. The concentration of the haloes in the hydrodynamical simulations is consistent with N-body expectations up to Milky Way-mass galaxies, at which mass the haloes become twice as concentrated as compared with pure dark matter runs. This mass-dependent density profile can be directly applied to rotation curve data of observed galaxies and to semi-analytic galaxy formation models as a significant improvement over the commonly used NFW profile.
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“In particular, these authors found that the α , β and γ parameters in Equation 13 depend only on the stellar-to-halo mass ratio, and thus leaving only two free parameters, ρ 0 and R s .”
From The milky way total mass profile as inferred from Gaia DR2 (2020) · cites this paper as Di Cintio et al. 2014
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Metadata
- Authors
- Cintio, A. D., Brook, C. B., Dutton, A. A., Macciò, A. V., Stinson, G. S., & Knebe, A.
- Affiliations
- Universidad Autónoma de Madrid (ES)
- Max Planck Institute for Astronomy (DE)
- Corresponding author
- Cintio, Arianna Di
- Venue
- Monthly Notices of the Royal Astronomical Society
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-05-24
- Volume
- 441
- Issue
- 4
- Pages
- 2986-2995
- ISSN
- 0035-8711 (ISSN-L)
- 1365-2966
- OpenAlex
- W2125094672
- Cited by
- 294
- References
- 91
- Citations by year
- 2026: 20 · 2025: 23 · 2024: 21 · 2023: 17 · 2022: 29 · 2021: 26 · 2020: 26 · 2019: 27 · 2018: 31 · 2017: 21 · 2016: 27 · 2015: 21 · 2014: 5
- FWCI
- 13.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Monthly Notices of the Royal Astronomical Society (publishedVersion)
- CLOK (University of Central Lancashire) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- arXiv (Cornell University)
- Funders
- University of Central Lancashire
- European Commission (222919)
- Deutsche Forschungsgemeinschaft (subproject A1), SFB881, CSD2009-00064)
- Ministerio de Economía y Competitividad (AYA2012-31101, CSD-2009-00064, DP140100198)
- Ministerio de Ciencia e Innovación (Multi-Dark CSD2009-00064, CSD2009, CSD2009-00064)
- Max-Planck-Institut für Astronomie
- Science and Technology Facilities Council (ST/K000373/1)
- Australian Research Council (DP130100117, DP140100198)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Cosmology and Gravitation Theories
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Dark Matter and Cosmic Phenomena
- Keywords
- dark matter halos, halo density profiles, stellar-to-halo mass ratio, baryonic processes, galaxy formation, halo concentration, fluid simulation, rotation curves, Navarro-Frenk-White profile, semi-analytic galaxy formation models
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