The SAMI Galaxy Survey: Using concentrated star formation and stellar population ages to understand environmental quenching

Contributors: Di Wang, Scott Croom, Julia Bryant, Adam Schaefer, Matt Owers, Claudia Lagos, Nic Scott


This project will expand on the previous work of Schaefer et al (2017, 2019). We will study how spatially resolved star formation depends on environment by using different parameters to investigate the star formation quenching as a function of environment (including cluster, group and field). Environmental quenching is the suppression of star formation in high-density regions. While the existence of such quenching has been known for some time, the detailed physical mechanisms remain hard to discern. Together with this environmental quenching, there is a clear trend for higher-mass galaxies to have lower star formation rates. Some work shows that at least in some cases, environmental quenching is an outside-in process (e.g. Koopman & Kenny 2006; Schaefer et al. 2017, 2019).

We will use the unitless scale-radius ratio (r50,Hα/r50,cont) (building on earlier work of Schaefer et al. 2017,2019) and star formation rate as main parameters. We will use this metric to investigate quenching across a much enlarged dynamic range of environment (from clusters to the field), and with the full SAMI Galaxy Survey sample. This unitless scale-radius ratio shows the ratio of ongoing star formation to previous (or quenched) star formation. We will show our results using different environment density (group mass, local density, projected phase-space) and as a function of galaxy mass. We will also use the correlation between Dn4000, Hdelta absorption and Halpha to show star formation rate on different time scales to know how quickly the star formation is quenching (as Dn4000 has longer time-scale compare with Halpha). We will also combine our measurements with other star formation rate indicators from the GAMA Survey (e.g. UV, far infrared) to place better constraints on the timescale of quenching in our galaxies.

We will examine how these vary with various environments related properties including: i) environment density ii) SFR iii) location within the group/clusters (e.g. group/cluster radius). We will attempt to correct for AGN contamination using techniques similar to Davies et al.

We will use the full SAMI data and compare with GAMA field/group data plus available cluster data

This proposed paper is part of the PhD thesis of Di Wang (supervisors Scott Croom, Julia Bryant Claudia Lagos). 


Publication Date: 
July 2022