SEAGLE--III: Towards resolving the mismatch in the dark-matter fraction in early-type galaxies between simulations and observations
Sampath Mukherjee, L\'eon V. E. Koopmans, Crescenzo Tortora, Matthieu, Schaller, R. Benton Metcalf, Joop Schaye, Georgios Vernardos

TL;DR
This study compares dark-matter fractions in early-type galaxies from observations and simulations, finding good agreement with EAGLE but discrepancies with Illustris and IllustrisTNG, highlighting the impact of feedback models.
Contribution
It demonstrates that EAGLE simulations align well with observations, suggesting feedback models are key to resolving dark-matter fraction discrepancies in galaxy simulations.
Findings
EAGLE matches observed dark-matter fractions within galaxies.
Illustris and IllustrisTNG show lower dark-matter fractions than observations.
Differences in stellar feedback models likely cause the discrepancies.
Abstract
The central dark-matter fraction of galaxies is sensitive to feedback processes during galaxy formation. Strong gravitational lensing has been effective in the precise measurement of the dark-matter fraction inside massive early-type galaxies. Here, we compare the projected dark-matter fraction of early-type galaxies inferred from the SLACS strong-lens survey, with those obtained from the EAGLE, Illustris, and IllustrisTNG hydro-dynamical simulations. Previous comparisons with some simulations revealed a large discrepancy, with considerably higher inferred dark-matter fractions -- by factors 2-3 -- inside half of the effective radius in observed strong-lens galaxies as compared to simulated galaxies. Here, we report good agreement between EAGLE and SLACS for the dark-matter fractions inside both half of the effective radius and the effective radius as a function of the galaxy's stellar…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
