2017
DOI: 10.1007/s10509-017-3171-3
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Two phase formation of massive elliptical galaxies: study through cross-correlation including spatial effect

Abstract: Area of study is the formation mechanism of the present-day population of elliptical galaxies, in the context of hierarchical cosmological models accompanied by accretion and minor mergers. The present work investigates the formation and evolution of several components of the nearby massive early-type galaxies (ETGs) through cross-correlation function (CCF), using the spatial parameters right ascension (RA) and declination (DEC), and the intrinsic parameters mass (M * ) and size. According to the astrophysical… Show more

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Cited by 20 publications
(13 citation statements)
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“…Among the controversy that the number of natural groups in GRBs is two or three, we apply kernel principal component analysis (Schölkopf and Smola, 2002) to GRB data set to perform clustering as well as dimension and noise reduction. Previous work of kernel principal component analysis on astronomical data includes classification of supernovae (Ishida et al, 2012(Ishida et al, , 2013, denoising of early-type galaxies (Modak et al, 2016), etc. Kernel principal component analysis is a nonlinear transformation on raw data, where nonlinear features are extracted from data in terms of kernel principal components.…”
Section: Introductionmentioning
confidence: 99%
“…Among the controversy that the number of natural groups in GRBs is two or three, we apply kernel principal component analysis (Schölkopf and Smola, 2002) to GRB data set to perform clustering as well as dimension and noise reduction. Previous work of kernel principal component analysis on astronomical data includes classification of supernovae (Ishida et al, 2012(Ishida et al, , 2013, denoising of early-type galaxies (Modak et al, 2016), etc. Kernel principal component analysis is a nonlinear transformation on raw data, where nonlinear features are extracted from data in terms of kernel principal components.…”
Section: Introductionmentioning
confidence: 99%
“…For unsupervised classifications some attempts have been made by K-means cluster analysis on the basis of all the parameters ( Ellis et al (2005); ; ; Mondal et al (2008); Chattopadhyay et al (2009); Babu et al (2009); Almeida et al (2010); Fraix-Burnet et al (2010); Fraix-Burnet et al (2012); De et al (2016); Modak et al (2017); Modak et al (2020)).…”
Section: Introductionmentioning
confidence: 99%
“…In most of the previous studies authors retrieved data from sdss and they performed supervised classification. ICA has been used widely for source separation (Pires et al (2006); Pike et al (2017); Martins-Filho et al (2018); Sheldon and Richards (2018)) and dimensionality reduction (Richardson et al (2016); Sarro et al (2018)) but rarely for unsupervised classification (Mu (2007); Das et al (2015); Modak et al (2017); Modak et al (2020)).…”
Section: Introductionmentioning
confidence: 99%
“…Astronomical data, coming from different sources and collected by different telescopes, are often needed to be combined in a complete data set for study. In this situation, it is always very important to test compatibility of two data sets, collected in different surveys or measured with different resolutions, before pooling them together and they can only be combined when they are compatible (see, for example, De et al, 2014;Modak et al, 2017). That means, they should have approximately the same amount of observational error on an average.…”
Section: Introductionmentioning
confidence: 99%
“…De et al, 2014;Modak et al, 2017). Here, data are collected from different sources and therefore needed to be checked for compatibility before pooling them together for further study.…”
mentioning
confidence: 99%