Relationships Between Stellar Velocity Dispersion and the Atmospheres of Early-Type Galaxies
R. L. S. Frisbie, M. Donahue, G. M. Voit, K. Lakhchaura, N. Werner, M., Sun

TL;DR
This study tests the Voit et al. (2020) black hole feedback model by analyzing Chandra X-ray data of early-type galaxies, finding that galaxy atmospheric properties generally match model predictions, especially in specific subsamples.
Contribution
It provides empirical validation of the Voit et al. (2020) model using X-ray observations and refines understanding of galaxy atmospheres based on stellar velocity dispersion.
Findings
Galaxies with higher velocity dispersion have steeper pressure, density, and entropy profiles.
The model's predictions are more accurate for galaxies with low central entropy.
Agreement improves for galaxies with velocity dispersion between 220 and 300 km/s.
Abstract
The Voit et al. (2020) black hole feedback valve model predicts relationships between stellar velocity dispersion and atmospheric structure among massive early-type galaxies. In this work, we test that model using the Chandra archival sample of 49 early-type galaxies from Lakhchaura et al. (2018). We consider relationships between stellar velocity dispersion and entropy profile slope, multiphase gas extent, and the ratio of cooling time to freefall time. We also define subsamples based on data quality and entropy profile properties that clarify those relationships and enable more specific tests of the model predictions. We find that the atmospheric properties of early-type galaxies generally align with the predictions of the Voit et al. (2020) model, in that galaxies with greater stellar velocity dispersion tend to have radial profiles of pressure, gas density, and entropy with steeper…
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.
