We present an in-depth analysis of gas morphologies for a sample of 25 Milky Way–like galaxies from the IllustrisTNG TNG50 simulation. We constrain the morphology of cold, warm, hot gas, and gas particles as a whole using a local shell iterative method and explore its observational implications by computing the hard-to-soft X-ray ratio, which ranges between 10−3 and 10−2 in the inner ∼50 kpc of the distribution and 10−5–10−4 at the outer portion of the hot gas distribution. We group galaxies into three main categories: simple, stretched, and twisted. These categories are based on the radial reorientation of the principal axes of the reduced inertia tensor. We find that a vast majority (77%) of the galaxies in our sample exhibit twisting patterns in their radial profiles. Additionally, we present detailed comparisons between (i) the gaseous distributions belonging to individual temperature regimes, (ii) the cold gas distributions and stellar distributions, and (iii) the gaseous distributions and dark matter (DM) halos. We find a strong correlation between the morphological properties of the cold gas and stellar distributions. Furthermore, we find a correlation between gaseous distributions with a DM halo that increases with gas temperature, implying that we may use the warm–hot gaseous morphology as a tracer to probe the DM morphology. Finally, we show gaseous distributions exhibit significantly more prolate morphologies than the stellar distributions and DM halos, which we hypothesize is due to stellar and active galactic nucleus feedback.
Gas Morphology of Milky Way–like Galaxies in the TNG50 Simulation: Signals of Twisting and Stretching
Abstract
Copyright and License
© 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
The SAO REU program is funded in part by the National Science Foundation REU and Department of Defense ASSURE programs under NSF grant Nos. AST 1852268 and 2050813, and by the Smithsonian Institution. We also thank Matthew Ashby, Shep Doeleman, Yakov Faerman, Jonathan McDowell, and Jessica Werk for insightful conversations and invaluable comments. The authors thank the referee for their feedback, which greatly improved the quality of this manuscript. R.E. acknowledges the support from the Institute for Theory and Computation at the Center for Astrophysics as well as grant Nos. NASA ATP 21-atp21-0077, NSF AST-1816420, and HST-GO-16173.001-A for very generous support. The authors thank the supercomputer facility at Harvard, where most of the simulation work was done. M.V. acknowledges support through an MIT RSC award, a Kavli Research Investment Fund, NASA ATP grant No. NNX17AG29G, and NSF grant Nos. AST-1814053, AST-1814259, and AST-1909831. The TNG50 simulation was realized with compute time granted by the Gauss Center for Supercomputing (GCS) under GCS Large-Scale Projects GCS-DWAR on the GCS share of the supercomputer Hazel Hen at the High-Performance Computing Center Stuttgart (HLRS).
Software References
illustris_python (Nelson et al. 2019a, 2019b; Pillepich et al. 2019), SciPy_python (Oliphant 2007), Matplotlib (Hunter 2007), Numpy (Harris et al. 2020), Py-SPHViewer (Benitez-Llambay 2015), Python Programming Language (van Rossum 1995), and TNG50 (Nelson et al. 2019a, 2019b; Pillepich et al. 2019)
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Additional details
- ISSN
- 1538-4357
- National Science Foundation
- AST-1852268
- National Science Foundation
- AST-2050813
- Smithsonian Institution
- National Aeronautics and Space Administration
- 21-atp21-0077
- National Science Foundation
- AST-1816420
- National Aeronautics and Space Administration
- NASA Hubble Fellowship HST-GO-16173.001-A
- National Aeronautics and Space Administration
- NNX17AG29G
- National Science Foundation
- AST-1814053
- National Science Foundation
- AST-1814259
- National Science Foundation
- AST-1909831
- Massachusetts Institute of Technology
- Caltech groups
- TAPIR