Multivariate Study of the Star Formation Rate in Galaxies: Bimodality Revisited
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Subjective classification of galaxies can mislead us in the quest of the origin regarding formation and evolution of galaxies. Multivariate analyses are the best tools used for such kind of purpose to better understand the differences between various objects, in an objective manner. In the present study an objective classification of 362 923 galaxies of the Value Added Galaxy Catalogue (VAGC) is carried out with the help of three methods of multivariate analysis. First, independent component analysis (ICA) is used to determine a set of derived independent variables that are linear combinations of various observed parameters (viz. ionized lines, Lick indices, photometric and morphological parameters, star formation rates etc.) of the galaxies. Subsequently, K-means cluster analysis (CA) is applied on the independent components to find the optimum number of homogeneous groups. Finally, a stepwise multiple regression is carried out on each group to predict and study the star formation rate as a function of other independent observables. The properties of the ten groups thus uncovered, are used to explain their formation and evolution mechanisms. It is suggested that three groups are young and metal poor, belonging to the blue sequence, three others are old and metal rich (red sequence). The remaining four groups of intermediate ages cannot be classified in this bimodal sequence: two belong to a pronounced mixture of early and late type galaxies whereas the other two mostly contain old early type galaxies. The above result is indicative of a continuous evolutionary scenario of galaxies instead of two discrete modes, blue and red, so far suggested by previous authors. Some of our groups occupy the transition region with different quenching mechanisms. This establishes the elegance of a multivariate analysis giving rise to a sophisticated refinement over subjective inference.
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