学术文献库

Theoretical models are vital for exploring the galaxy merger process, which plays a crucial role in the evolution of galaxies. Recent advances in modelling have placed tight constraints on the buildup of stellar material in galaxies across cosmic time. Despite these successes, extracting the merger rates from observable data remains a challenge. Differences in modelling techniques, combined with limited observational data, drive conflicting conclusions on the merging timescales of close pairs. We employ an empirical model for galaxy formation that links galaxy properties to the growth of simulated dark matter halos, along with mock lightcone galaxy catalogues, to probe the dependencies of pair merging probabilities and merging timescales. In this work, we demonstrate that the pair merging probabilities are best described by a logistic function and that mean merging timescales can be well approximated by a linear relation in the projected separation and line of sight velocity difference in observed pairs. Together, our fitting formulae can accurately predict merger rates from galaxy pairs to at least $z\sim4$ under a wide variety of pair selection criteria. Additionally, we show that some commonly used pair selection criteria may not represent a suitable sample of galaxies to reproduce underlying merger rates. Finally, we conclude from our analysis that observation timescales are primarily driven by dynamics and are not strongly impacted by the star formation properties of the component galaxies.