The Production And Evolution Of Scaling Laws Via Galaxy Merging PDF Download

Major galaxy merging is a fundamental aspect of the hierarchical structure-growth scenario of the universe, and it is theoretical expected to contribute to several key aspects of galaxy evolution. As such, empirically identifying major mergers is a key methodological step towards assessing the ``merging -- galaxy evolution'' connection, and close-pair and morphology-based methods are established empirical merger identification techniques. Yet, the merger rate measurements from these methods vary up to a factor of five owing to their unique but analogous systematic biases, especially during the key epoch of galaxy growth (7-11 Gyr ago), highlighting that the merger contribution to galaxy growth remains poorly constrained. As a step towards addressing key open questions pertaining to empirical merger identification methodologies, we carryout comprehensive analysis of close pairs and merging induced tidal features (and in general galactic substructures) using forefront observational data from the Hubble Space Telescope (HST) and realistic mock observations from leading theoretical simulations. We analyze the incidence of major, similar-mass (mass ratio4) close pairs among a large sample of ~9800 massive galaxies (log Mstellar/Msun 10.3) from the HST-CANDELS survey and quantify the major merger rate evolution over 11 Gyr in cosmic history (published in Mantha et al., 2018). Using the mock light cone data from the leading SantaCruz Semi-Analytical Model (SAM), we systematically analyze the impact of different observational effects on the measurement of close-pair frequency and provide detailed statistical corrections to account for them. We also developed a new public software tool to extract and quantify different kinds of faint morphological substructures hosted by massive galaxies in the HST imaging and demonstrated its applicability in extracting tidal features using mock observations of a galaxy merger from a cosmological simulation (published in Mantha et al., 2019). Finally, using supervised and unsupervised deep-learning models, we also investigate the automated characterization of different morphological substructures hosted within the parametric light-profile subtracted residual images of 10,000 massive galaxies from the HST CANDELS survey.