The SAMI galaxy survey: Detection of environmental dependence of galaxy spin in observations and simulations using marked correlation functions

Tomas H. Rutherford, Scott M. Croom, Jesse van de Sande, Claudia P. del Lagos, Joss Bland-Hawthorn, S. Brough, Julia J. Bryant, Francesco D’Eugenio, Matt S. Owers

Research output: Contribution to journalArticlepeer-review

5 Citations (Web of Science)

Abstract

The existence of a kinematic morphology–density relation remains uncertain, and instead stellar mass appears the more dominant driver of galaxy kinematics. We investigate the dependence of the stellar spin parameter proxy lRe on environment using a marked cross-correlation method with data from the Sydney Australian Astronomical Observatory Multi-object Integral-field Spectrograph (SAMI) Galaxy Survey. Our sample contains 710 galaxies with spatially resolved stellar velocity and velocity dispersion measurements. By utilizing the highly complete spectroscopic data from the Galaxy and Mass Assembly Survey, we calculate marked cross-correlation functions for SAMI galaxies using a pair count estimator and marks based on stellar mass and lRe. We detect an anticorrelation of stellar kinematics with environment at the 3.2σ level, such that galaxies with low lRe values are preferably located in denser galaxy environments. However, a significant correlation between stellar mass and environment is also found (correlation at 2.4σ), as found in previous works. We compare these results to mock observations from the cosmological Evolution and Assembly of Galaxies and their Environments (EAGLE) simulations, where we find a similar significant lRe anticorrelation with environment, and a mass and environment correlation. We demonstrate that the environmental correlation of lRe is not caused by the mass–environment relation. The significant relationship between lRe and environment remains when we exclude slow rotators. The signals in SAMI and EAGLE are strongest on small scales (10–100 kpc) as expected from galaxy interactions and mergers. Our work demonstrates that the technique of marked correlation functions is an effective tool for detecting the relationship between lRe and environment.

Original languageEnglish
Article number84
JournalAstrophysical Journal
Volume918
Issue number2
DOIs
Publication statusPublished - 10 Sept 2021

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