ΛCDM cosmology predicts the hierarchical formation of galaxies, which build up mass by merger events and accreting smaller systems. The stellar halo of the Milky Way (MW) has proven to be useful a tool for tracing this accretion history. However, most of this work has focused on the outer halo where dynamical times are large and the dynamical properties of accreted systems are preserved. In this work, we investigate the inner galaxy regime, where dynamical times are relatively small and systems are generally completely phase mixed. Using the FIRE-2 and Auriga cosmological zoom-in simulation suites of MW-mass galaxies, we find the stellar density profiles along the minor axis (perpendicular to the galactic disk) within the Navarro–Frenk–White scale radii (R ≈ 15 kpc) are best described as an exponential disk with scale height < 0.3 kpc and a power-law component with slope α ≈ −4. The stellar density amplitude and slope for the power-law component are not significantly correlated with metrics of the galaxy's accretion history. Instead, we find the stellar profiles strongly correlate with the dark matter profile. Across simulation suites, the galaxies studied in this work have a stellar-to-dark-matter mass ratio that decreases as 1/r2 along the minor axis.
Cosmological Predictions for Minor Axis Stellar Density Profiles in the Inner Regions of Milky Way–mass Galaxies
Abstract
Copyright and License
© 2025. 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
This material is based upon work supported by the National Science Foundation under Award No. 2303831. We have used simulations from the Auriga Project public data release (R. J. J. Grand et al. 2024) available at https://wwwmpa.mpa-garching.mpg.de/auriga/data.
Software References
Astropy (Astropy Collaboration et al. 2013, 2018), Matplotlib (J. D. Hunter 2007), IPython (F. Pérez & B. E. Granger 2007), Numpy (C. R. Harris et al. 2020), Scipy (P. Virtanen et al. 2020), GizmoAnalysis (A. Wetzel & S. Garrison-Kimmel 2020a), HaloAnalysis (A. R. Wetzel et al. 2016; A. Wetzel & S. Garrison-Kimmel 2020b).
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Additional details
- National Science Foundation
- 2303831
- Accepted
-
2025-02-23
- Available
-
2025-03-21Published
- Caltech groups
- TAPIR, Division of Physics, Mathematics and Astronomy (PMA)
- Publication Status
- Published