Dust-Obscured Galaxy Protocluster and Cluster Survey (DOGPACS): Identifying Large-Scale Structures 9−10 Billion Light-Years Away
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Abstract
Primordial density fluctuations have grown over time due to gravitational instability to form galaxies and, eventually, large-scale structures, such as clusters of galaxies. Galaxy clusters are the most massive collapsed structures in the universe. During cluster formation, the largest aggregation of gas, galaxies, and dark matter passes through an intermediate phase called the protocluster. Over the past decades, many studies have identified distant clusters and protoclusters due to advanced observational strategies. However, the protocluster-to-cluster transformation is still unclear, mainly due to the lack of large samples of early-stage clusters and late-stage protoclusters. Our research has identified a large selection of nearly 300 galaxy cluster candidates at redshift 1.3 < z < 1.8 (9-10 billion light-years away) during the formation epoch of the galaxy clusters. This study leverages the fact that high-z galaxy clusters and protoclusters exhibit enhanced star-formation and AGN activity in their cores. The candidates are identified using a sample of highly star- forming and/or AGN Ultra-Luminous Infrared Galaxies called the Dust-Obscured Galaxies (DOGs) as signposts in the Spitzer Deep Wide-Field Survey (SDWFS) in Bootes. A two-point correlation function analysis demonstrates that the sample has a mass scale of the galaxy clusters. Using a more multi-wavelength SDWFS catalog, this study has also uncovered a proto-supercluster structure at z = 1.75 (10 billion light-years away). This proto-supercluster is a bound structure hosting dozens of protoclusters and clusters of galaxies, including the most massive galaxy cluster (IDCS J1426.5+3508) found to date at z > 1.5. Follow-up studies of this supercluster will provide a comprehensive picture of protocluster-to-cluster transition and the evolution of its constituent gas and galaxies. Finally, we develop and implement a novel machine learning technique to determine the photometric redshift (photo-z) of the galaxies using a decision tree-based architecture as part of the mission work for the upcoming Euclid Space Mission (2023). The photo-z results are comparable with many other template fitting- and machine learning-based methods.
Table of Contents
Galaxy cluster candidates signposted by dust-obscured galaxies as 1.3≤z≤1.8 -- A proto-supercluster candidate hosting a massive galaxy cluster at z-1.75 -- Estimating photometric redshift probability distribution function using gradient boosted regression tree (GBIT)
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Ph.D. (Doctor of Philosophy)