Multivariate Approaches to Classification in Extragalactic Astronomy

Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous mono-or bivariate classifications most often made by eye. However, a classification must be driven by the data, and sophisticated multivariate statistical tools are used more and more often. In this paper we review these different approaches in order to situate them in the general context of unsupervised and supervised learning. We insist on the astrophysical outcomes of these studies to show that multivariate analyses provide an obvious path toward a renewal of our classification of galaxies and are invaluable tools to investigate the physics and evolution of galaxies.Comment: Open Access paper. http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>. \<10.3389/fspas.2015.00003 \&g

Statistical Evidence for Three classes of Gamma-ray Bursts

Two different multivariate clustering techniques, the K-means partitioning method and the Dirichlet process of mixture modeling, have been applied to the BATSE Gamma-ray burst (GRB) catalog, to obtain the optimum number of coherent groups. In the standard paradigm, GRB are classified in only two groups, the long and short bursts. However, for both the clustering techniques, the optimal number of classes was found to be three, a result which is consistent with previous statistical analysis. In this classification, the long bursts are further divided into two groups which are primarily differentiated by their total fluence and duration and hence are named low and high fluence GRB. Analysis of GRB with known red-shifts and spectral parameters suggests that low fluence GRB have nearly constant isotropic energy output of 10^{52} ergs while for the high fluence ones, the energy output ranges from 10^{52} to 10^{54} ergs. It is speculated that the three kinds of GRBs reflect three different origins: mergers of neutron star systems, mergers between white dwarfs and neutron stars, and collapse of massive stars.Comment: 7 pages, accepted for publication in the Astrophysical Journal. Minor editorial change

Clustering large number of extragalactic spectra of galaxies and quasars through canopies

International audienceCluster analysis is the distribution of objects into different groups or more precisely the partitioning of a data set into subsets (clusters) so that the data in subsets share some common trait according to some distance measure. Unlike classi cation, in clustering one has to rst decide the optimum number of clusters and then assign the objects into different clusters. Solution of such problems for a large number of high dimensional data points is quite complicated and most of the existing algorithms will not perform properly. In the present work a new clustering technique applicable to large data set has been used to cluster the spectra of 702248 galaxies and quasars having 1540 points in wavelength range imposed by the instrument. The proposed technique has successfully discovered ve clusters from this 702248X1540 data matrix

Uncovering the formation of ultra-compact dwarf galaxies by multivariate statistical analysis

We present a statistical analysis of the properties of a large sample of dynamically hot old stellar systems, from globular clusters to giant ellipticals, which was performed in order to investigate the origin of ultra-compact dwarf galaxies. The data were mostly drawn from Forbes et al. (2008). We recalculated some of the effective radii, computed mean surface brightnesses and mass-to-light-ratios, estimated ages and metallicities. We completed the sample with globular clusters of M31. We used a multivariate statistical technique (K-Means clustering), together with a new algorithm (Gap Statistics) for finding the optimum number of homogeneous sub-groups in the sample, using a total of six parameters (absolute magnitude, effective radius, virial mass-to-light ratio, stellar mass-to-light ratio and metallicity). We found six groups. FK1 and FK5 are composed of high- and low-mass elliptical galaxies respectively. FK3 and FK6 are composed of high-metallicity and low-metallicity objects, respectively, and both include globular clusters and ultra-compact dwarf galaxies. Two very small groups, FK2 and FK4, are composed of Local Group dwarf spheroidals. Our groups differ in their mean masses and virial mass-to-light ratios. The relations between these two parameters are also different for the various groups. The probability density distributions of metallicity for the four groups of galaxies is similar to that of the globular clusters and UCDs. The brightest low-metallicity globular clusters and ultra-compact dwarf galaxies tend to follow the mass-metallicity relation like elliptical galaxies. The objects of FK3 are more metal-rich per unit effective luminosity density than high-mass ellipticals.Comment: Accepted for publication in Astrophysical Journa