Optimizing Numerical Simulations of Colliding Galaxies. I. Fitness Functions and Optimization Algorithms

Gravitational n -body models can be used to simulate the dynamical evolution of colliding galaxies. Given observational data in the form of images of the galaxies, we seek to estimate the true values of various dynamical parameters through the careful application of optimization methods. However, optimizing these models can be quite difficult due to (1) their expensive run-time, (2) the dimensionality and complexity of the parameter space which must be explored, (3) the presence of morphological symmetries which introduce many false extrema into the fitness landscape, and (4) the fact that the majority of orbital trajectories produce systems with little to no tidal features. To combat these issues, we have developed novel “two-factor” fitness functions for image similarity quantification and an adaptive genetic algorithm with crossover and mutation operators designed to handle parameter correlation. We test these methods by fitting to a synthetic target image.