Abstract

I will present new results on observed galaxy mergers using a few different surveys and projects. The first is a galaxy pair methodology within the REFINE survey – an analysis of the 3 deepest large near-infrared extragalactic fields. I will discuss the role of both major and minor mergers in the formation of galaxies up to z~6. I will then discuss new results using machine learning on the Illustris cosmological simulation to determine the merger history with deep learning by training a machine on the simulation and then applying the supervised learning to resolved imaging data from the Hubble Space Telescope. I will show that these two methods of finding mergers are consistent and provides further evidence for the existence of dark matter in a new way. I will also discuss how these results provide unique information about the origin of gravitational waves discovered by LIGO /VIrgo and predictions for the number of sources that LISA will discover at high redshift.