A Multiwavelength Analysis of the Effect of Cluster Environments on Galaxy Evolution
In this work, we present analysis to constrain the role of local environment on galaxy evolu- tion. The hot, dense galaxy cluster environment is utilized as a comparative laboratory to galaxies that reside in the less dense field where it is ‘alone’. Within clusters, the buildup of red, passive galaxies is observed to be in excess of the field, yet the mechanisms responsible for this graveyard of galaxies remain to be clearly identified. This indicates that external gas quenching in the cluster environment such as through ram-pressure stripping is potentially dominating over any internal processes that deplete the gas supply. Observations focusing on the cores of clusters reveal that galaxies are typically already quenched and in a passive state, indicating that the processes respon- sible occur while the galaxy is located in the outskirts. Therefore, expanding observations beyond the cores is crucial towards deriving a more representative sample across varying cosmic densities and is more likely to capture the site of galaxy transformation in situ. With current telescope capa- bility ranging from spectroscopy to deep imaging, a thorough inspection of individual members of a galaxy cluster is feasible.This study combines the power of ground-based wide-field imaging with space-based slitless spectroscopy to deliver a multifaceted look into galaxies in clusters as they were more than 5 billion years ago. This is accomplished in 2 distinct ways: 1) determination of the passive fraction through a binary classification in UVJ color-color space to determine whether a galaxy is star-forming or quiescent and 2) further refine these efforts with Hα-based star-formation rate observations from the Hubble Space Telescope (HST)/WFC3. The first portion of this thesis is devoted towards an aggregate view of the EDisCS clusters, where cluster membership is defined to at least the infall radius of each galaxy cluster. This data sample consists of 13 mid-mass clusters in the range 0.4 90%) quiescent at all stellar masses and redshifts. Additionally, the passive fraction displays a radial dependence at higher z (0.6 90% are desirable, this neural network proves that grism data can be trained and automatically classified in less than half the time it would take a human to complete the same task. In conclusion, we have demonstrated 2 powerful and complementary tools to investigate the role of environment galaxy evolution in clusters. The star-formation rate results revealing UVJ- quiescent core galaxies with significant Hα emission contributes an interesting point towards how much weight can be given to passive fraction results. With the latter, we are only able to de- termine rest-frame colors that generally correlate with star-formation. However, core galaxies, which have been the culprit of higher proportions of ‘dead’ galaxies in clusters, may actually have ongoing star-formation, which is evident in the lack of differences between environments on the main sequence diagram. The galaxy may appear redder for a variety of reasons including AGN, heated gas, or that they are in the process of quenching. Further observations of galaxies in various environments comparing rest-frame colors and star-formation rates for galaxies, along with high resolution spectra, will help refine this result.
Astrophysics, Physics, Galaxy Clusters, Galaxy Evolution, Machine Learning
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