Conclusions

Galaxy mergers are a very fundamental and important process when discussing galaxy evolution. In the currently accepted \(\Lambda \) CDM framework for structure formation, large scale structures are considered to evolve through hierarchical growth. In the context of galaxies, one such avenue of hierarchical growth is through galaxy mergers. As such, in order to understand the evolution of galaxies, it is necessary to understand galaxy mergers and their associated processes, such as star formation, chemical evolution, and AGN activity. However, our understanding of the role that mergers can play within galaxy evolution and associated processes is still heavily contested. One main reason for the disagreements in theories lies in galaxy merger identification methods. In order to conduct studies that can enrich our understanding of galaxy mergers, a method to accurately identify mergers, with high completeness and precision, must be employed. Various methods have been used in a variety of studies, with all methods having their own strengths and limitations. No matter the method, due to the wide variety of merger galaxies, the removal of interlopers whilst simultaneously achieving a high completeness is is a complex task. In this thesis, we approach merger identification in large-scale galaxy surveys through various methods, and use the merger samples identified through these methods to conduct merger-related studies. Chap. 2 is a modified version of “The bimodality in the mass-metallicity relation in SDSS-MaNGA galaxy pairs”, Kiyoaki Christopher Omori and Tsutomu T. Takeuchi, 2022, Astronomy & Astrophysics, Article Number 145, Volume 660, April 2022, and Chap. 3 is a modified version of “Galaxy mergers in Subaru HSC-SSP: a deep representation learning approach for identification and the role of environment on merger incidence”, Kiyoaki Christopher Omori, Connor Bottrell, Mike Walmsley, Hassen M. Yesuf, Andy D. Goulding, Xuheng Ding, Gergö Popping, John D. Silverman, Tsutomu T. Takeuchi, and Yoshiki Toba, 2023, Astronomy & Astrophysics, Article Number 142, Volume 679, November 2023. We summarize our methods and associated studies.