Paper Info
Title
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.
Year
DOI
Venue
2010
10.4018/jaec.2010100102
International Journal of Applied Evolutionary Computation
Keywords
Field
DocType
linear combination,model spectrum,galaxy spectrum,galaxy spectra,sdss data release,parameter extraction,stellar population parameter,evolution strategies,original spectrum,different theoretical model,stellar population synthesis,optimization problem,perform stellar population synthesis,galaxy formation,parameter space,astronomical surveys,spectrum,evolution strategy,stellar evolution,galaxies,information need,data analysis,evolutionary computation
Astrophysics,Linear combination,Astronomical survey,Computer science,Galaxy formation and evolution,Artificial intelligence,Parameter space,Model spectrum,Galaxy,Stellar population,Machine learning,Stellar evolution
Journal
Volume
Issue
Citations 
1
4
0
PageRank 
References 
Authors
0.34
2
2
Name
Order
Citations
PageRank
Juan Carlos Gomez18412.89
Olac Fuentes224634.55