相关论文: Quasar Main Sequence: a line or a plane?

We study the geometry of the Hbeta broad emission region by comparing the M_BH values derived from Hbeta through the virial relation with those obtained from the host galaxy luminosity in a sample of 36 low redshift (z around 0.3) quasars.…

Sparse principal component analysis (sparse PCA) is a widely used technique for dimensionality reduction in multivariate analysis, addressing two key limitations of standard PCA. First, sparse PCA can be implemented in high-dimensional low…

We present an unsupervised learning analysis of correlation hierarchies in the quarter-filled simple and extended Hubbard models by applying principal component analysis (PCA) to exact-diagonalization (ED) data on 3x4 and 4x4 cylindrical…

We present a study aimed at understanding the physical phenomena underlying the formation and evolution of galaxies following a data-driven analysis of spectroscopic data based on the variance in a carefully selected sample. We apply…

Absorption line systems detected in quasar spectra can be used to compare the value of the fine-structure constant, {\alpha}, measured today on Earth with its value in distant galaxies. In recent years, some evidence has emerged of small…

We have used the VLA FIRST survey and the APM catalog of the POSS-I plates as the basis for constructing a new radio-selected sample of optically bright quasars. This is the first radio-selected sample that is competitive in size with…

The relation between star formation rates and stellar masses, i.e. the galaxy main sequence, is a useful diagnostic of galaxy evolution. We present the distributions relative to the main sequence of 55 optically-selected PG and 12…

Broad emission lines in quasars enable us to "resolve" structure and kinematics of the broad line emitting region (BLR) thought to in- volve an accretion disk feeding a supermassive black hole. Interpretation of broad line measures within…

The problem of principle component analysis (PCA) is traditionally solved by spectral or algebraic methods. We show how computing the leading principal component could be reduced to solving a \textit{small} number of well-conditioned {\it…

Recently discovered quasar pairs at high redshifts ($z\gtrsim$5) are likely precursors to supermassive black hole mergers, providing a promising window to high redshift quasar growth mechanisms. However, the large uncertainties on their…

(abridged) Quasar absorption lines provide a precise test of the assumed constancy of the fundamental constants of physics. We have investigated potential changes in the fine-structure constant, alpha, and the proton-to-electron mass ratio,…

Principal component analysis is an important pattern recognition and dimensionality reduction tool in many applications. Principal components are computed as eigenvectors of a maximum likelihood covariance $\widehat{\Sigma}$ that…

We present the results of an HST and ground-based imaging study of a complete 3C sample of z ~ 1 sources, including 5 quasars and 5 radio galaxies. We have resolved continuum structure around all of our quasars in the WFPC2 images and in…

We present a reverberation mapping (RM) experiment that combines broad- and intermediate-band photometry; it is the first such attempt targeting a sample of 13 quasars at $0.2<z<0.9$. The quasars were selected to have strong H$\alpha$ or…

We present the spectra of 14 quasars with a wide coverage of rest wavelengths from 1000 to 7300 A. The redshift ranges from z = 0.061 to 0.555 and the luminosity from M_{B} = -22.69 to -26.32. We describe the procedure of generating the…

Conventional principal component analysis (PCA) finds a principal vector that maximizes the sum of second powers of principal components. We consider a generalized PCA that aims at maximizing the sum of an arbitrary convex function of…

Theoretically, bound binaries of massive black holes are expected as the natural outcome of mergers of massive galaxies. From the observational side, however, massive black hole binaries remain elusive. Velocity shifts between narrow and…

We propose a novel method of finding principal components in multivariate data sets that lie on an embedded nonlinear Riemannian manifold within a higher-dimensional space. Our aim is to extend the geometric interpretation of PCA, while…

Astronomy has evolved almost exclusively by the use of spectroscopic and imaging techniques, operated separately. With the development of modern technologies it is possible to obtain datacubes in which one combines both techniques…

A set of curves or images of similar shape is an increasingly common functional data set collected in the sciences. Principal Component Analysis (PCA) is the most widely used technique to decompose variation in functional data. However, the…