Effects of Galactic Environment on Size and Dark Matter Content in Low-mass Galaxies

Creators: Mercado, Francisco J.^{1, 2}; Moreno, Jorge^{1, 3}; Feldmann, Robert⁴; Zeender, Marckie¹; Benavides, José A.⁵; Piotrowska, Joanna M.²; Klein, Courtney⁶; Wheeler, Coral⁷; Necib, Lina⁸; Bullock, James S.⁶; Hopkins, Philip F.²

1. Pomona College
2. California Institute of Technology
3. Carnegie Observatories
4. University of Zurich
5. University of California, Riverside
6. University of California, Irvine
7. California State Polytechnic University
8. Massachusetts Institute of Technology

Abstract

We utilize the cosmological volume simulation FIREbox to investigate how a galaxy's environment influences its size and dark matter content. Our study focuses on approximately 1200 galaxies (886 central and 332 satellite halos) in the low-mass regime, with stellar masses between 10⁶ and 10⁹M_⊙. We analyze the size–mass relation (r₅₀–M_⋆), the inner dark matter mass–stellar mass (M_(DM)50–M_⋆) relation, and the halo mass–stellar mass (M_halo–M_⋆) relation. At fixed stellar mass, we find that galaxies experiencing stronger tidal influences, indicated by higher Perturbation Indices (PI > 1) are generally larger and have lower halo masses relative to their counterparts with lower Perturbation Indices (PI < 1). Applying a Random Forest regression model, we show that both the environment (PI) and halo mass (M_halo) are significant predictors of a galaxy's relative size and dark matter content. Notably, because M_halo is also strongly affected by the environment, our findings indicate that environmental conditions not only influence galactic sizes and relative inner dark matter content directly, but also indirectly, through their impact on halo mass. Our results highlight a critical interplay between environmental factors and halo mass in shaping galaxy properties, affirming the environment as a fundamental driver in galaxy formation and evolution.

Copyright and License

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

F.J.M. is funded by the National Science Foundation (NSF) Math and Physical Sciences (MPS) Award AST-2316748. J.S.B. is supported by NSF grant AST-2408246 and NASA grant 80NSSC22K0827. C.K. is supported by NSF Graduate Research Fellowship Program (GRFP) grant DGE-1839285 and NASA grant 80NSSC22K0827. L.N. is supported by the Sloan Fellowship, the NSF CAREER award 2337864, NSF award 2307788, and NSF award PHY-2019786 (from the NSF AI Institute for Artificial Intelligence and Fundamental Interactions, http://iaifi.org/). P.F.H. is supported by a Simons investigator award.

We thank Edwin J. Menendez for advising us in selecting colorblind-friendly color maps to use for our figures. Finally, we sincerely thank the referee for the thoughtful and constructive feedback, which has helped us improve this manuscript.

Software References

the functionalities provided by the following Python packages played a critical role in the analysis and visualizations presented in this paper: matplotlib (J. D. Hunter 2007), seaborn (M. L. Waskom 2021), Py-SPHViewer (A. Benitez-Llambay 2015), NumPy (S. Van Der Walt et al. 2011), scikit-learn (F. Pedregosa et al. 2011), SciPy (P. Virtanen et al. 2020), and iPython (F. Perez & B. E. Granger 2007).

Files

Mercado_2025_ApJ_983_93.pdf

Files (2.3 MB)

Name	Size	Download all
Mercado_2025_ApJ_983_93.pdf md5:58bb487e668d2312ac813c5aa0498935	2.3 MB	Preview Download

Additional details

	All versions	This version
Views	0	0
Downloads	0	0
Data volume	0 Bytes	0 Bytes