Fingerprints of the hierarchical building up of the structure on the gas kinematics of galaxies
Maria E. De Rossi (1,2,3), Patricia B. Tissera (1,2), Susana E., Pedrosa (1,2) ((1) Consejo Nacional de Investigaciones Cientificas y, Tecnicas, CONICET, Argentina, (2) Instituto de Astronomia y Fisica del, Espacio, IAFE, Argentina, (3) Facultad de Ciencias Exactas y Naturales,

TL;DR
This study uses cosmological simulations to explore how galaxy gas kinematics, influenced by hierarchical structure formation, affect the Tully-Fisher relation, highlighting the importance of combined velocity indicators and the impact of mergers.
Contribution
It demonstrates that combining rotation and dispersion velocities reduces scatter in Tully-Fisher relations and identifies the maximum rotation curve as the best velocity proxy across morphologies.
Findings
Dispersion-dominated systems show higher $\sigma / V_{rot}$ ratios.
Mergers increase scatter in the Tully-Fisher relation.
Maximum rotation velocity at the rotation curve peak minimizes scatter.
Abstract
Recent observational and theoretical works have suggested that the Tully-Fisher Relation might be generalised to include dispersion-dominated systems by combining the rotation and dispersion velocity in the definition of the kinematical indicator. Mergers and interactions have been pointed out as responsible of driving turbulent and disordered gas kinematics, which could generate Tully-Fisher Relation outliers. We intend to investigate the gas kinematics of galaxies by using a simulated sample which includes both, gas disc-dominated and spheroid-dominated systems. Cosmological hydrodynamical simulations which include a multiphase model and physically-motivated Supernova feedback were performed in order to follow the evolution of galaxies as they are assembled. Both the baryonic and stellar Tully-Fisher relations for gas disc-dominated systems are tight while, as more…
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.
