Using Evolution Strategies to Perform Stellar Population Synthesis for Galaxy Spectra from SDSS

Abstract

In this work, the authors employ Evolution Strategies (ES) to automatically extract a set of physical parameters, corresponding to stellar population synthesis, from a sample of galaxy spectra taken from the Sloan Digital Sky Survey (SDSS). This parameter extraction is presented as an optimization problem and being solved using ES. The idea is to reconstruct each galaxy spectrum by means of a linear combination of three different theoretical models for stellar population synthesis. This combination produces a model spectrum that is compared with the original spectrum using a simple difference function. The goal is to find a model that minimizes this difference, using ES as the algorithm to explore the parameter space. This paper presents experimental results using a set of 100 spectra from SDSS Data Release 2 that show that ES are very well suited to extract stellar population parameters from galaxy spectra. Additionally, in order to better understand the performance of ES in this problem, a comparison with two well known stochastic search algorithms, Genetic Algorithms (GA) and Simulated Annealing (SA), is presented.