UC Irvine

It has long been discovered that environment plays a major role in galaxy evolution, with galaxies in dense regions of the cosmos more likely to have their star formation suppressed or \textquote{quenched} relative to their more isolated counterparts. Despite this remarkable discovery, our current understanding of the physical processes responsible for the suppression (or \textquote{quenching}) of star formation remains woefully incomplete. This is especially true for galaxies that are members of massive galaxy groups and clusters – i.e. \textquote{satellite} galaxies – given that our best cosmological models struggle to reproduce key observations, such as the satellite quenched fraction. This dissertation aims to advance our knowledge in the areas of environmental quenching that are poorly understood, focusing on the quenching of low-mass dwarf satellite galaxies and satellite populations at intermediate and high redshifts. Observational studies of low-mass satellite quenching have been limited by their inability to robustly characterize the local environment and star-formation activity of faint systems. This work overcomes these limitations by combining supervised machine learning and statistical background subtraction techniques to constrain the satellite quenched fraction of group populations ($\mhalo = 10^{13-14}~\msun$) down to a satellite stellar mass limit of ${\sim}10^7~\msun$. This approach successfully reproduces the established quenched fraction trends at high-masses while finding that ram-pressure stripping -- the rapid removal of cold gas from the interstellar medium of satellites as they move through the hot and dense intra-group or intracluster medium permeating the host halo -- is the likely dominant quenching mechanism responsible for shutting down star formation in the low-mass regime. The importance of investigating intermediate and high redshift is that the vast majority of environmental quenching studies are limited to the very local Universe ($z \lesssim 0.1$). This work addresses this shortcoming by combining multi-wavelength spectroscopic observations of massive clusters ($\mhalo = 10^{14-15}~\msun$) at $z\gtrsim1$ with the infall histories of analogs from $N$-body simulations to model satellite quenching at this poorly studied epoch. The quenching timescales derived from this study indicate that \textquote{starvation} -- the slow depletion of cold gas in the absence of cosmological accretion after a satellite becomes a member of a more massive host -- is the dominant driver of environmental quenching at this epoch. However, this study lends support to the scenario where ram-pressure stripping potentially acts as a secondary quenching mechanism in massive clusters at this epoch. Overall, this work provides valuable insights into the quenching timescales and underlying mechanisms of satellite galaxies in massive clusters at $z\gtrsim1$, while introducing an impactful methodology that combines supervised machine learning and statistical background subtraction to study the quenching of low-mass satellites beyond the Local Group.