Evidence of pre-processing and a dependence on dynamical state for low-mass satellite galaxies

Abstract

We study the dependence of satellite star formation rate and morphology on group dynamics for a sample of SDSS groups. We classify the group dynamical state and study satellite properties for populations of galaxies at small and large group-centric radii. For galaxies at large radii we find no differences in the star-forming or disc fraction for those in Gaussian groups compared to those in non-Gaussian groups. By comparing the star-forming and disc fractions of infalling galaxies to field galaxies we find evidence for the pre-processing of both star formation rate and morphology. The strength of pre-processing increases with halo mass and is highest for low-mass galaxies infalling onto high-mass haloes. We show that the star formation rate of galaxies at small radii correlates with group dynamical state, with galaxies in nonGaussian groups showing enhanced star-forming fractions compared to galaxies in Gaussian groups. Similar correlations are not seen for the disc fractions of galaxies at small radii. This seems to suggest that either the mechanisms driving star formation quenching at small halo-centric radii are more efficient in dynamically relaxed groups, or that non-Gaussian groups have assembled more recently and therefore satellites of the groups will have been exposed to these transforming mechanisms for less time.

Connecting optical and X-ray tracers of galaxy cluster relaxation

“…Cassano et al 2010;Nurgaliev et al 2017). From the optical perspective, we also find that high-mass clusters have velocity distributions which are less Gaussian than low-mass systems, in agreement with previous studies (Roberts & Parker 2017;de Carvalho et al 2017). Although it is important to note that it is easier to statistically identify departures from normality for high-mass systems with many members.…”

“…In this work we use the AD statistic as a proxy for cluster relaxedness, where increasing values of A 2 are indicative of progressively more unrelaxed clusters. It is also common in the literature to use the p-value associated with the AD statistic to define a dichotomy between Gaussian and non-Gaussian clusters (e.g Hou et al 2009;Martínez & Zandivarez 2012;Roberts & Parker 2017), which we consider in Section 4.3.…”

“…The AD test has become a relatively common tool used to identify unrelaxed systems from large redshift surveys (e.g. Hou et al 2009;Ribeiro et al 2010;Martínez & Zandivarez 2012;Hou et al 2013;Ribeiro et al 2013;Roberts & Parker 2017), though its efficacy has only been tested in detail using Monte Carlo simulations sampling from idealized parent distributions (both Gaussian and non-Gaussian, Hou et al 2009;Ribeiro et al 2013). These tests have provided useful insight into the strengths and limitations of the AD test, but it is also important to test this technique in a more physical setting.…”

“…Ribeiro et al 2010Ribeiro et al , 2013. Previously, we have shown that low-mass galaxies in the inner regions of Gaussian (G) groups have reduced star-forming fractions relative to non-Gaussian (NG) groups (Roberts & Parker 2017). We have also shown that star-forming and disc fractions for low mass galaxies are enhanced in X-ray underluminous (XRW) groups, and show that galaxies XRW groups have velocity distributions consistent with being unrelaxed systems (at least relative to X-ray strong groups, Roberts et al 2016).…”

‘Observing’ unrelaxed clusters in dark matter simulations

“…It is apparent that there is a small excess of NG 1D clusters at the highest cluster masses. This excess has also been previously reported in samples of observed clusters (Roberts & Parker 2017;de Carvalho et al 2017). Given this dependence of classified dynamical state on cluster mass, it is important to mass-match the G 1D and NG 1D samples to ensure that any differences between G 1D and NG 1D clusters are not resulting from different cluster mass distributions.…”