Understanding the properties and diversity of Galaxies and Quasars through Spectral Decomposition

In the first part of this thesis, we study spectral decompositions of galaxies and quasars (QSOs) by the Karhunen-Lo`eve (KL) transformusing the Sloan Digital Sky Survey (SDSS). Our goal is to understand the average properties and sample variances of the data, with an eyetoward obtaining objective classifications of these objects.The eigencoefficients describing the galaxies naturally place the spectra into several classes defined by the plane formed by the firstthree eigencoefficients of each spectrum. Spectral types, corresponding to different Hubble-types and galaxies with extremeemission lines, are identified for 170,000 spectra and are shown to be complementary to existing spectral classifications. Biasin the spectral classifications due to the aperture spectroscopy in the SDSS is within the signal-to-noise limit for majority of galaxies.We extend the analysis to the decomposition of 17,000 QSO spectra in which the diversity is known to be larger. From a commonality analysis on the eigenspectra sets constructed using different QSO samples, we deduce that QSO spectral classification is redshift and luminosity dependent. The prominent redshift effect is found to be the evolution of the small bump. The luminosity effect is related to the Baldwin effect. We therefore perform the KL transforms in three cases: the local (in the vicinity of emission lines), the intermediate (redshift and luminosity binned) and the global(restframe 900-8000 angstrom) spectral bandpasses. We find that the second order QSO eigenspectra, in both the global and the intermediate spectral bandpasses, represent features from the host galaxies of the QSOs. We discuss the insights the results provide toward classification.In the second part of this thesis, we probe spectroscopic variability of galaxies and narrow line active galactic nuclei (AGNs) usingmulti-epoch observations in the SDSS. We study the galaxy variability per spectral type defined previously in the KL transform and in theOsterbrock diagram. The amplitude of galaxy variability is found to depend on spectral type. We show that the variability in theHII galaxies can be partly due to star formation; and that in the AGNs and the mean eClass type D are probably not related to starbursts

Quantifying correlations between galaxy emission lines and stellar continua

We analyse the correlations between continuum properties and emission line equivalent widths of star-forming and active galaxies from the Sloan Digital Sky Survey. Since upcoming large sky surveys will make broad-band observations only, including strong emission lines into theoretical modelling of spectra will be essential to estimate physical properties of photometric galaxies. We show that emission line equivalent widths can be fairly well reconstructed from the stellar continuum using local multiple linear regression in the continuum principal component analysis (PCA) space. Line reconstruction is good for star-forming galaxies and reasonable for galaxies with active nuclei. We propose a practical method to combine stellar population synthesis models with empirical modelling of emission lines. The technique will help generate more accurate model spectra and mock catalogues of galaxies to fit observations of the new surveys. More accurate modelling of emission lines is also expected to improve template-based photometric redshift estimation methods. We also show that, by combining PCA coefficients from the pure continuum and the emission lines, automatic distinction between hosts of weak active galactic nuclei (AGNs) and quiescent star-forming galaxies can be made. The classification method is based on a training set consisting of high-confidence starburst galaxies and AGNs, and allows for the similar separation of active and star-forming galaxies as the empirical curve found by Kauffmann et al. We demonstrate the use of three important machine learning algorithms in the paper: k-nearest neighbour finding, k-means clustering and support vector machines.Comment: 14 pages, 14 figures. Accepted by MNRAS on 2015 December 22. The paper's website with data and code is at http://www.vo.elte.hu/papers/2015/emissionlines

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.Comment: 36 Pages, 13 Figures, 4 Tables. AJ Accepte

Reliable Eigenspectra for New Generation Surveys

We present a novel technique to overcome the limitations of the applicability of Principal Component Analysis to typical real-life data sets, especially astronomical spectra. Our new approach addresses the issues of outliers, missing information, large number of dimensions and the vast amount of data by combining elements of robust statistics and recursive algorithms that provide improved eigensystem estimates step-by-step. We develop a generic mechanism for deriving reliable eigenspectra without manual data censoring, while utilising all the information contained in the observations. We demonstrate the power of the methodology on the attractive collection of the VIMOS VLT Deep Survey spectra that manifest most of the challenges today, and highlight the improvements over previous workarounds, as well as the scalability of our approach to collections with sizes of the Sloan Digital Sky Survey and beyond.Comment: 7 pages, 3 figures, accepted to MNRA

Generating on-the-fly large samples of theoretical spectra through N-dimensional grid

Many analyses and parameter estimations undertaken in astronomy require a large set (> 10^5) of non-analytical, theoretical spectra, each of these defined by multiple parameters. We describe the construction of an N-dimensional grid which is suitable for generating such spectra. The theoretical spectra are designed to correspond to a targeted parameter grid but otherwise to random positions in the parameter space, and they are interpolated on-the-fly through a pre-calculated grid of spectra. The initial grid is designed to be relatively low in parameter resolution and small in occupied hard disk space and therefore can be updated efficiently when a new model is desired. In a pilot study of stellar population synthesis of galaxies, the mean square errors on the estimated parameters are found to decrease with the targeted grid resolution. This scheme of generating a large model grid is general for other areas of studies, particularly if they are based on multi-dimensional parameter space and are focused on contrasting model differences.Comment: 7 pages, 5 figures, 1 table. Accepted for publication in A

Spectral Decomposition of Broad-Line AGNs and Host Galaxies

Using an eigenspectrum decomposition technique, we separate the host galaxy from the broad line active galactic nucleus (AGN) in a set of 4666 spectra from the Sloan Digital Sky Survey (SDSS), from redshifts near zero up to about 0.75. The decomposition technique uses separate sets of galaxy and quasar eigenspectra to efficiently and reliably separate the AGN and host spectroscopic components. The technique accurately reproduces the host galaxy spectrum, its contributing fraction, and its classification. We show how the accuracy of the decomposition depends upon S/N, host galaxy fraction, and the galaxy class. Based on the eigencoefficients, the sample of SDSS broad-line AGN host galaxies spans a wide range of spectral types, but the distribution differs significantly from inactive galaxies. In particular, post-starburst activity appears to be much more common among AGN host galaxies. The luminosities of the hosts are much higher than expected for normal early-type galaxies, and their colors become increasingly bluer than early-type galaxies with increasing host luminosity. Most of the AGNs with detected hosts are emitting at between 1% and 10% of their estimated Eddington luminosities, but the sensitivity of the technique usually does not extend to the Eddington limit. There are mild correlations among the AGN and host galaxy eigencoefficients, possibly indicating a link between recent star formation and the onset of AGN activity. The catalog of spectral reconstruction parameters is available as an electronic table.Comment: 18 pages; accepted for publication in A