Adjustment of observational data to specific functional forms using a particle swarm algorithm and differential evolution: Rotational curves of a spiral galaxy as case study

Abstract

The fitting of experimental or observational data to specific functional forms requires high computational capacities in order to tackle the complexity of the calculations. This complexity makes compulsory the use of efficient search procedures such as evolutionary algorithms. Evolutionary algorithms have proved their capability to find suboptimal, high-quality solutions to problems with large search spaces. In this context, a particle swarm algorithm and differential evolution are used to fit a data set to a serial expansion of Legendre polynomials. Concerning the data set, 56 rotation curves of spiral galaxies are used to build up a serial expansion—physically meaningless—retaining the essential information of the curves. The ultimate goal of this work is twofold: first, to provide a theoretical functional form representing the features of the rotational curves of spiral galaxies in order to couple it to other computational models; and second, to demonstrate the applicability of evolutionary algorithms to the matching between astronomical data sets and theoretical models.