Characterizing Ultra-Diffuse Galaxies Across Environment

Abstract

Ultra-diffuse galaxies (UDGs) are a class of diffuse, spatially-extended galaxies. Their existence challenges the conventional framework of galaxy evolution.Outside of an isolated handful of previously known examples of UDGs, the astronomy community in the 1980s and 1990s largely failed to uncover this hidden population of low surface brightness (LSB) galaxies, halting the progress of studying galaxy formation and evolution in this extremely LSB regime. However, over the past few decades, improvements in computing capabilities, telescope technologies, and data reduction techniques have enabled the development of extremely deep imaging surveys, covering large fractions of the sky. Such surveys have achieved sufficient depth to uncover a large number of UDGs in galaxy clusters and other environments, sparking questions regarding their unique properties, their survival in some of the harshest and most extreme environments, and their formation and evolutionary mechanisms. Due to the extremely low surface brightnesses of UDGs, obtaining spectra of (and thereby distances to) UDGs becomes an arduous task. Yet, acquiring accurate distance information is of utmost importance to identify UDGs, constrain their environments, and study their properties in context with their environment. In addition, understanding the environment's role in shaping their observed properties provides constraints on proposed UDG formation scenarios. As such, throughout the work presented in this dissertation, we focused our efforts on measuring and estimating redshifts to perform statistical analysis of UDGs. Initially, we acquired spectroscopic redshifts for 5 UDGs (when only one in Coma was available at the time). We showed that most of the Dragonfly UDG sample was indeed associated with the Coma cluster. Furthermore, we compared our spectra with stellar population synthesis models and showed that the spectra of UDGs were consistent with being composed of intermediate age, metal-poor stellar populations. In our next study, in addition to the growing spectroscopic sample of UDGs available in the literature, we expanded this spectroscopic sample to 44 UDGs after observing an additional 19 candidates in the Systematically Measuring Ultra-Diffuse Galaxies (SMUDGes) survey with the LBT. We observed color differences in UDGs as a function of environment, but we otherwise detected no differences in structural parameters relative to the environment. We noted some correlations between photometric and structural parameters in our dataset. Acquiring spectroscopic redshifts is extremely expensive, and as such, we developed a technique to statistically estimate the redshifts of UDGs. We conducted the same analysis from our previous studies on objects from the SMUDGes Survey with statistical redshifts. Similar to our initial results obtained by analyzing objects with spectroscopic redshifts, we observed no differences in the structural parameters of UDGs across environment. However, the environment's relationship with UDG color appears to be more complex than initially observed. Most scaling relations of photometric and structural parameters in UDGs appear to be consistent with those of dwarf galaxies. Lastly, we developed a machine learning technique to analyze the morphology of UDG candidates, grouping candidates on similar morphological features. We use this method to identify potential high redshift targets and filter out low redshift, spurious sources from the SMUDGes survey.