Using evolution strategies to perform stellar population synthesis for galaxy spectra from SDSS
Current surveys from modern astronomical observatories contain a huge amount of data; in particular, the Sloan Digital Sky Survey (SDSS) has reached the order of terabytes of data in images and spectra. Such an amount of information needs to be exploited by sophisticated algorithms that automatically analyze the data in order to extract useful knowledge from the mega databases. In this work we employ evolution strategies (ES) to automatically extract a set of physical parameters corresponding to stellar population synthesis (ages, metallicities, reddening and relative contributions) from a sample of galaxy spectra taken from SDSS. Such parameters are useful in cosmological studies and for understanding galaxy formation, composition, and evolution. We pose this parameter extraction as an optimization problem and then solve it using ES. The idea is to reconstruct each galaxy spectrum from the sample by means of a linear combination of three similar theoretical models, each contributing in a different way to the stellar population synthesis. This linear 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. We present 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.