Calibration of semi-analytic models of galaxy formation using Particle Swarm Optimization

Understand

We present a fast and accurate method to select an optimal set of parameters in semi-analytic models of galaxy formation and evolution (SAMs).

• Our approach compares the results of a model against a set of observables applying a stochastic technique called Particle Swarm Optimization (PSO), a self-learning algorithm for localizing regions of maximum likelihood in multidimensional spaces that outperforms traditional sampling methods in terms of computational cost.
• We apply the PSO technique to the SAG semi-analytic model combined with merger trees extracted from a standard $\Lambda$CDM N-body simulation.
• The calibration is performed using a combination of observed galaxy properties as constraints, including the local stellar mass function and the black hole to bulge mass relation.