Extracting stellar population parameters of galaxies from photometric data using evolution strategies and locally weighted linear regression

Abstract

There is now a huge amount of high quality photometric data available in the literature whose analysis is bound to play a fundamental role in studies of the formation and evolution of structure in the Universe. One important problem that this large amount of data generates is the definition of the best procedure or strategy to achieve the best result with the minimum of computational time. Here we focus on the optimization of methods to obtain stellar population parameters (ages, proportions, redshift and reddening) from photometric data using evolutionary synthesis models. We pose the problem as an optimization problem and we solve it with Evolution Strategies (ES). We also test a hybrid algorithm combining Evolution Strategies and Locally Weighted Linear Regression (LWLR). The experiments show that the hybrid algorithm achieves greater accuracy, and faster convergence than evolution strategies. On the other hand the performance of ES and ES-LWLR is similar when noise is added to the input data.