A fast inversion method of parameters for contact binaries based on differential evolution

Abstract

With the development of modern astronomical observation techniques and contact binary research, a large number of light curves of contact binaries have been published, and it has become a challenge to quickly derive the basic physical parameters of contact binaries from their light curves. This article presents a neural network (NN) based on the differential evolution intelligent optimization algorithm to infer the fundamental physical parameters of contact binaries from their light curve. Based on a large dataset of light curves and parameter data generated by Phoebe, a NN mapping model is established, while Differential Evolution (DE) and Markov Chain Monte Carlo (MCMC) algorithms are used to find reasonable parameter combinations, respectively. The experiments show that the parameter inversion speed of the DE algorithm is approximately 50% faster than that of the MCMC algorithm, while guaranteeing a parameter accuracy at least consistent with the those of MCMC algorithm.