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.

[1]  L. Sodré,et al.  Semi‐empirical analysis of Sloan Digital Sky Survey galaxies – I. Spectral synthesis method , 2005 .

[2]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[3]  Hans-Georg Beyer,et al.  Toward a Theory of Evolution Strategies: Self-Adaptation , 1995, Evolutionary Computation.

[4]  D. Schlegel,et al.  Maps of Dust IR Emission for Use in Estimation of Reddening and CMBR Foregrounds , 1997, astro-ph/9710327.

[5]  Olac Fuentes,et al.  Obtaining the phase of an interferogram by use of an evolution strategy: part I. , 2002, Applied optics.

[6]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[7]  Alexander Outkin,et al.  FinSim: A Framework for Modeling Financial System Interdependencies , 2008 .

[8]  Olac Fuentes,et al.  Using Evolution Strategies to Find a Dynamical Model of the M81 Triplet , 2004, KES.

[9]  M. Endler,et al.  International Journal of Adaptive, Resilient and Autonomic Systems , 2022 .

[10]  D. Schlegel,et al.  Maps of Dust Infrared Emission for Use in Estimation of Reddening and Cosmic Microwave Background Radiation Foregrounds , 1998 .

[11]  J. W. Atmar,et al.  Comparing genetic operators with gaussian mutations in simulated evolutionary processes using linear systems , 1990, Biological Cybernetics.

[12]  Alessandro Bressan,et al.  Spectrophotometric evolution of elliptical galaxies. 1: Ultraviolet excess and color-magnitude-redshift relations , 1994 .

[13]  Ang Yang,et al.  Applications of Complex Adaptive Systems , 2008 .

[14]  Marsha J. Wolf,et al.  Ages and Metallicities of Extragalactic Globular Clusters from Spectral and Photometric Fits of Stellar Population Synthesis Models , 2006, astro-ph/0610261.

[15]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[16]  Pascal Lafon,et al.  A Comparison of Evolutionary Algorithms for Mechanical Design Components , 2002 .

[17]  J. Mathis,et al.  The relationship between infrared, optical, and ultraviolet extinction , 1989 .

[18]  K. Abazajian et al..  The Second Data Release of the Sloan Digital Sky Survey , 2004 .

[19]  Walterio W. Mayol-Cuevas,et al.  Simultaneous stabilization using evolutionary strategies , 1997 .

[20]  Daniel A. Ashlock,et al.  Evolutionary computation for modeling and optimization , 2005 .