OBJECTIVE IDENTIFICATION OF INFORMATIVE WAVELENGTH REGIONS IN GALAXY SPECTRA

Understanding the diversity in spectra is the key to determining the physical parameters of galaxies. The optical spectra of galaxies are highly convoluted with continuum and lines which are potentially sensitive to different physical parameters. Defining the wavelength regions of interest is therefore an important question. In this work, we identify informative wavelength regions in a single-burst stellar populations model by using the CUR Matrix Decomposition. Simulating the Lick/IDS spectrograph configuration, we recover the widely used Dn(4000), Hbeta, and HdeltaA to be most informative. Simulating the SDSS spectrograph configuration with a wavelength range 3450-8350 Angstrom and a model-limited spectral resolution of 3 Angstrom, the most informative regions are: first region-the 4000 Angstrom break and the Hdelta line; second region-the Fe-like indices; third region-the Hbeta line; fourth region-the G band and the Hgamma line. A Principal Component Analysis on the first region shows that the first eigenspectrum tells primarily the stellar age, the second eigenspectrum is related to the age-metallicity degeneracy, and the third eigenspectrum shows an anti-correlation between the strengths of the Balmer and the Ca K and H absorptions. The regions can be used to determine the stellar age and metallicity in early-type galaxies which have solar abundance ratios, no dust, and a single-burst star formation history. The region identification method can be applied to any set of spectra of the user's interest, so that we eliminate the need for a common, fixed-resolution index system. We discuss future directions in extending the current analysis to late-type galaxies.

[1]  Sandra M. Faber,et al.  Variations in Spectral-Energy Distributions and Absorption-Line Strengths among Elliptical Galaxies , 1973 .

[2]  J. Mould.  The MG index in old galaxy populations - A theoretical approach , 1978 .

[3]  A. G. Bruzual.  Spectral evolution of galaxies. 1. Early-type systems , 1983 .

[4]  David Burstein,et al.  Old stellar populations. I. A spectroscopic comparison of galactic globular clusters, M 31 globular clusters, and elliptical galaxies. , 1984 .

[5]  David Burstein,et al.  Old stellar populations. II. an analysis of K-giant spectra. , 1985 .

[6]  A. Schmidt,et al.  On the meaning of a minimization procedure applied to a degenerate astrophysical problem , 1989 .

[7]  Frederic H. Chaffee,et al.  An objective classification scheme for QSO spectra , 1992 .

[8]  Audra E. Kosh,et al.  Linear Algebra and its Applications , 1992 .

[9]  Sandra M. Faber,et al.  MG and Fe absorption features in elliptical galaxies , 1992 .

[10]  David Burstein,et al.  Old stellar populations. 5: Absorption feature indices for the complete LICK/IDS sample of stars , 1994 .

[11]  G. Worthey,et al.  New Age Indicators for Old Stellar Populations , 1995 .

[12]  A. Szalay,et al.  Spectral classification of galaxies: An Orthogonal approach , 1994, astro-ph/9411044.

[13]  L. Girardi,et al.  Evolutionary sequences of stellar models with new radiative opacities. VI. $Z=0.0001$ , 1996 .

[14]  D. Ottaviani,et al.  Hγ and Hδ Absorption Features in Stars and Stellar Populations , 1997 .

[15]  David Burstein,et al.  Old Stellar Populations. VI. Absorption-Line Spectra of Galaxy Nuclei and Globular Clusters , 1997, astro-ph/9712258.

[16]  Hia,et al.  Differential Galaxy Evolution in Cluster and Field Galaxies at z ≈ 0.3 , 1999, astro-ph/9906470.

[17]  E. al.,et al.  The Sloan Digital Sky Survey: Technical summary , 2000, astro-ph/0006396.

[18]  R. Nichol,et al.  Stellar masses and star formation histories for 105 galaxies from the Sloan Digital Sky Survey , 2002, astro-ph/0204055.

[19]  The Baldwin effect and black hole accretion: A spectral principal component analysis of a complete quasar sample , 2002, astro-ph/0211641.

[20]  G. Chabrier.  Galactic Stellar and Substellar Initial Mass Function , 2003, astro-ph/0304382.

[21]  D. Madgwick,et al.  Spectroscopic Detection of Type Ia Supernovae in the Sloan Digital Sky Survey , 2003, astro-ph/0310887.

[22]  G. Bruzual,et al.  Stellar population synthesis at the resolution of 2003 , 2003, astro-ph/0309134.

[23]  Andreas Korn,et al.  Higher‐order Balmer line indices in α/Fe‐enhanced stellar population models , 2004 .

[24]  R. Nichol,et al.  Distributions of Galaxy Spectral Types in the Sloan Digital Sky Survey , 2004, astro-ph/0407061.

[25]  A. Szalay,et al.  Spectral Classification of Quasars in the Sloan Digital Sky Survey: Eigenspectra, Redshift, and Luminosity Effects , 2004, astro-ph/0408578.

[26]  Andrew J. Connolly,et al.  Spectral decomposition of broad-line agns and host galaxies , 2005 .

[27]  Paul C. Hewett,et al.  Peering through the OH forest: a new technique to remove residual sky features from Sloan Digital Sky Survey spectra , 2005 .

[28]  Iap,et al.  The ages and metallicities of galaxies in the local universe , 2005, astro-ph/0506539.

[29]  Porto,et al.  Bursty stellar populations and obscured active galactic nuclei in galaxy bulges , 2007, 0706.3113.

[30]  R. Peletier,et al.  Stellar Populations as Building Blocks of Galaxies , 2007 .

[31]  Michael W. Mahoney,et al.  PCA-Correlated SNPs for Structure Identification in Worldwide Human Populations , 2007, PLoS genetics.

[32]  S. Muthukrishnan,et al.  Relative-Error CUR Matrix Decompositions , 2007, SIAM J. Matrix Anal. Appl..

[33]  A. Szalay,et al.  Reliable eigenspectra for new generation surveys , 2008, 0809.0881.

[34]  Petros Drineas,et al.  CUR matrix decompositions for improved data analysis , 2009, Proceedings of the National Academy of Sciences.

[35]  C. Yip.  GENERATING ON-THE-FLY LARGE SAMPLES OF THEORETICAL SPECTRA THROUGH AN N-DIMENSIONAL GRID , 2009, 0911.1280.

[36]  A. Szalay,et al.  CORRELATIONS BETWEEN NEBULAR EMISSION AND THE CONTINUUM SPECTRAL SHAPE IN SDSS GALAXIES , 2011, 1110.1877.

[37]  A. Connolly,et al.  SPECTROSCOPIC DETERMINATION OF THE LOW-REDSHIFT TYPE Ia SUPERNOVA RATE FROM THE SLOAN DIGITAL SKY SURVEY , 2011, 1102.1485.

[38]  Michael W. Mahoney.  Randomized Algorithms for Matrices and Data , 2011, Found. Trends Mach. Learn..

[39]  J. Brinkmann,et al.  Evolution of the most massive galaxies to z= 0.6 – I. A new method for physical parameter estimation , 2011, 1108.4719.

[40]  Ashok N. Srivastava,et al.  Advances in Machine Learning and Data Mining for Astronomy , 2012 .

[41]  Brazil,et al.  QUALITATIVE INTERPRETATION OF GALAXY SPECTRA , 2012, 1207.3928.

[42]  G. Zamorani,et al.  The VIMOS Public Extragalactic Redshift Survey (VIPERS): spectral classification through principal component analysis , 2012, 1207.4374.