Background
Recent work by Peng, Maiolino & Chochrane 2015 and Trussler et al. have studied the offset in the mass - stellar metallicity plane between star-forming and quiescent galaxies. At fixed mass, quiescent galaxies tend to be more metal enriched than star-forming galaxies, with a difference of around 0.2 dex in metallicity at log(M*)~10.
These papers explain this difference by conjecturing that galaxy quenching is in general a slow process. During normal star formation, a galaxy will accrete metal-poor gas from its halo and the cosmic web, which tends to dilute the metallicity of its interstellar medium (ISM). On the other hand, each generation of stars will return a fraction of its metals to the ISM (via winds and supernovae) which increases the ISM's metallicity. Since newly formed stars reflect the metallicity of the gas from which they are formed, the balance of these two processes over time sets the general evolution of a galaxy's stellar metallicity (e.g. Peng & Maiolino 2014).
If a galaxy is cut off from accreting further metal-poor gas, the enrichment due to star-formation is left unregulated and the galaxy will quickly increase its stellar metallicity. As it slowly runs out of gas in its disc, the galaxy's star formation rate will decrease until it eventually becomes quenched. This means that such a slow quenching process will lead a galaxy to slightly increase its stellar mass but markedly increase its metallicity. On the other hand, if a galaxy's star formation is quenched very quickly, its stellar mass and stellar metallicity no longer evolve and are fixed at their values at quenching (ignoring the effects of mergers, rejuvenation, etc). This scenario naturally explains an offset in the mass-metallicity plane: the process which leads galaxies to become quenched also explains why quenched galaxies sit in a different area of parameter space to star-forming galaxies.
Project Description
This project presents observations which call this interpretation into question. Using my spatially-resolved metallicity measurements from a stellar population analysis of the SAMI sample (see my previous project for more details), I have measured a central metallicity for 1905 SAMI galaxies. I recover the same offset in metallicity between star-forming and quiescent galaxies as found in Peng et al. 2015 and Trussler et al. 2020.
The work of Barone et al. 2018 and Barone et al. 2020, however, found that a galaxy's stellar metallicity correlates much more tightly with its gravitational potential rather than its stellar mass. I find that my sample is very neatly separated in the gravitational potential-metallicity plane, with quiescent galaxies following exactly the same relation as star-forming galaxies but just at larger values of gravitational potential. This means, however, that quiescent galaxies and star-forming galaxies at the same mass are not comparable- quiescent galaxies will tend to be smaller, which the Peng et al. 2015 evolution model does not account for.
Furthermore, I make a simple toy model which simulates how a population of galaxies may appear if they all quenched instantaneously. I place galaxies on the mass-size plane at redshifts around 2 and evolve them forward in steps of 10 Myrs. During each timestep, they form a fixed amount of stars, grow in size according to delta R ~ 0.3 delta M (van Dokkum et al. 2015) and increase their metallicity according to the potential-metallicity relation I've measured. This simulates the growth of star-forming galaxies across cosmic time.
I model quenching by having galaxies randomly stop their growth in mass, size and metallicity at the start of each timestep. As their gravitational potential increases, the probability that a galaxy quenches during a given timestep increases. This quenching is "fast" in the sense that it happens on timescales of ~10Myrs. Using this fast-quenching model, I can qualitatively reproduce the mass-metallicity, potential-metallicity and mass-size planes of my SAMI observations.
Possible Overlap
Many papers have measured stellar population gradients in SAMI and compared them to various quantities (e.g. Barone et al. 2018, Ferreras et al. 2019). However, to my knowledge the interpretation/discussion of the data in this work doesn't overlap with previous SAMI projects.
Progress
The analysis for this project is almost finished, and I am currently writing a draft which should be sent round for comments in the coming weeks.
