Add to ORKGPrincipal Component Analysis (PCA) is a well-known multivariate technique used to decorrelate a set of vectors. PCA has been extensively applied in the past to the classification of stellar and galaxy spectra. Here we apply PCA to the optical spectra of early-type galaxies, with the aim of extracting information about their star formation history. We consider two different data sets: 1) a reduced sample of 30 elliptical galaxies in Hickson compact groups and in the field, and 2) a large volume-limited (z<0.1) sample of ~7,000 galaxies from the Sloan Digital Sky Survey. Even though these data sets are very different, the homogeneity of the populations results in a very similar set of principal components. Furthermore, most of the information...
We present a Principal Component Analysis (PCA)-based spectral classification, eta, for the first 56...
International audienceWe propose to describe the variety of galaxies from the Sloan Digital Sky Surv...
We apply Principal Component Analysis (PCA) to study the variability of the X-ray continuum in the S...
Principal Component Analysis (PCA) is a well-known multivariate technique used to decorrelate a set ...
Environmental differences in the stellar populations of early-type galaxies are explored using princ...
We present a study aimed at understanding the physical phenomena underlying the formation and evolut...
Using a volume-limited sample of ~7,000 early-type galaxies from the Sloan Digital Sky Survey (SDSS)...
Principal component analysis (PCA) is being extensively used in Astronomy but not yet exhaustively e...
We describe the technique of principal components analysis (PCA) as applied to the analysis of varia...
The study of stellar populations in galaxies is particularly interesting, since they are a fossil re...
We develop a Principal Component Analysis aimed at classifying a subset of 27 350 spectra of galaxie...
Galaxy spectra are a useful diagnostic tool that can be used to reveal the intrinsic properties of g...
We develop a Principal Component Analysis aimed at classifying a subset of 27 350 spectra of galaxie...
Context. Ongoing and future surveys of variable stars will require new techniques to analyse their l...
We show that the first 10 eigencomponents of the Karhunen-Loève expansion or Principal Component Ana...
We present a Principal Component Analysis (PCA)-based spectral classification, eta, for the first 56...
International audienceWe propose to describe the variety of galaxies from the Sloan Digital Sky Surv...
We apply Principal Component Analysis (PCA) to study the variability of the X-ray continuum in the S...
Principal Component Analysis (PCA) is a well-known multivariate technique used to decorrelate a set ...
Environmental differences in the stellar populations of early-type galaxies are explored using princ...
We present a study aimed at understanding the physical phenomena underlying the formation and evolut...
Using a volume-limited sample of ~7,000 early-type galaxies from the Sloan Digital Sky Survey (SDSS)...
Principal component analysis (PCA) is being extensively used in Astronomy but not yet exhaustively e...
We describe the technique of principal components analysis (PCA) as applied to the analysis of varia...
The study of stellar populations in galaxies is particularly interesting, since they are a fossil re...
We develop a Principal Component Analysis aimed at classifying a subset of 27 350 spectra of galaxie...
Galaxy spectra are a useful diagnostic tool that can be used to reveal the intrinsic properties of g...
We develop a Principal Component Analysis aimed at classifying a subset of 27 350 spectra of galaxie...
Context. Ongoing and future surveys of variable stars will require new techniques to analyse their l...
We show that the first 10 eigencomponents of the Karhunen-Loève expansion or Principal Component Ana...
We present a Principal Component Analysis (PCA)-based spectral classification, eta, for the first 56...
International audienceWe propose to describe the variety of galaxies from the Sloan Digital Sky Surv...
We apply Principal Component Analysis (PCA) to study the variability of the X-ray continuum in the S...