The role of stochastic and smooth processes in regulating galaxy quenching

Abstract

Context. Galaxies can be classified as passive ellipticals or star-forming discs. Ellipticals dominate at the high end of the mass range, and therefore there must be a mechanism responsible for the quenching of star-forming galaxies. This could either be due to the secular processes linked to the mass and star formation of galaxies or to external processes linked to the surrounding environment. However, the contribution from these smooth and stochastic processes to galaxy quenching has yet to be quantified. Aims. In this paper, we analytically model the processes that govern galaxy evolution and quantify their contribution. The key advantage of our method is that we do not assume the strength of the contribution from any of these processes beforehand, but instead aim to find their efficiencies. We have specifically studied the effects of mass quenching, gas stripping, and mergers on galaxy quenching. Methods. To achieve this, we first assumed a set of differential equations that describe the processes that shape galaxy evolution. We then modelled the parameters of these equations by maximising likelihood. These equations describe the evolution of galaxies individually, but the parameters of the equations are constrained by matching the extrapolated intermediate-redshift galaxies with the low-redshift galaxy population. In this study, we modelled the processes that change star formation and stellar mass in massive galaxies from the GAMA survey between z ≈ 0.4 and the present. Results. We identified and quantified the contributions from mass quenching, gas stripping, and mergers to galaxy quenching. By modelling mass quenching, we found that quenching begins for galaxies above a mass of ≈1010.2 M⊙, but is dependent on the gas accretion rate before quenching. The quenching timescale is on average 1.2 Gyr and a closer look reveals support for the slow-then-rapid quenching scenario. The major merging rate of galaxies is about once per 10 Gyr, while the rate of ram pressure stripping is significantly higher. In galaxies with decreasing star formation, we show that star formation is lost to fast quenching mechanisms such as ram pressure stripping and is countered by mergers, at a rate of about 41% Gyr−1 and to mass quenching 49% Gyr−1. Therefore, slow quenching mechanisms have a greater influence on galaxies in group or cluster environments than fast quenching mechanisms.

Death at watersheds: Galaxy quenching in low-density environments

“…These authors showed that the dominant mechanism is inside-out quenching where quenching first takes place in the central region and then proceeds outwards. This effect is dominant for central galaxies and highmass satellites, but is weaker for low-mass satellites with stellar masses logM * < 10.5 (see also Kipper et al 2021). This agrees well with our results, which showed that the percentage of galaxies with log M * < 10.5 is lower than 20 % among satellite galaxies with D n (4000) > 1.75, and there are almost no BGGs with such low stellar mass.…”

“…Typically, it takes more than one peripassage to quench a satellite galaxy. The star formation in galaxies deceases under environmental processes described as slow-then-rapid quenching with characteristic timescales of 4 − 5 Gyr (Muzzin et al 2014;McGee et al 2014;Maier et al 2019;Kipper et al 2021;Kuschel et al 2022). Slow quenching by strangulation and overconsumption starts when a galaxy enters a group and gas inflow stops.…”

The miniJPAS survey

“…Another scenario is the 'slow-then-rapid' quenching (see e.g. Maier et al 2019;Roberts et al 2019;Kipper et al 2021), whereby galaxies undergo slow quenching processes and, once they enter a dense environment, start a faster quenching phase. Results from Pallero et al (2022), using the C-EAGLE simulation support this scenario.…”

Non-equilibrium in the Solar Neighbourhood using dynamical modelling with Gaia DR2

“…( 10) but implemented with different grids in kernel K. The g indexes the likelihoods, and G is the number of grids averaged per likelihood evaluation. A more thorough description is provided in Kipper et al (2021) in the context of modelling galaxy evolution.…”