Related papers: Classification and redshift estimation by principa…

Classification of intermediate redshift ($z$ = 0.3--0.8) emission line galaxies as star-forming galaxies, composite galaxies, active galactic nuclei (AGN), or low-ionization nuclear emission regions (LINERs) using optical spectra alone was…

Improving distance measurements in large imaging surveys is a major challenge to better reveal the distribution of galaxies on a large scale and to link galaxy properties with their environments. Photometric redshifts can be efficiently…

A galaxy's stellar mass is one of its most fundamental properties, but it remains challenging to measure reliably. With the advent of very large optical spectroscopic surveys, efficient methods that can make use of low signal-to-noise…

Our HST/NICMOS Pa survey of the Galactic center (GC) provides a uniform, panoramic, high-resolution map of stars and ionized diffuse gas in the central 416 arcmin^2 of the Galaxy. This survey was carried out with 144 HST orbits using two…

We present a selection of candidates of clusters and protoclusters of galaxies identified in the photometric data of the HSC-SSP Wide Public Data Release 3 (PDR3), spanning the redshift range $\rm 0.1 \leq z \leq 2$. The selection method,…

We introduce a Principal Component Analysis (PCA)--Bayesian framework for extracting chemical freeze-out conditions in relativistic heavy-ion collisions that resolves long-standing ambiguities in hadron-ratio--based analyses. By…

Principal Component Analysis (PCA) is a dimension reduction technique. It produces inconsistent estimators when the dimensionality is moderate to high, which is often the problem in modern large-scale applications where algorithm…

Redshift is a key quantity for inferring cosmological model parameters. In photometric redshift estimation, cosmologists use the coarse data collected from the vast majority of galaxies to predict the redshift of individual galaxies. To…

We discuss various aspects of centimeter and millimeter wavelength continuum and line observations of high redshift star forming galaxies. Perhaps the most important lesson is that sensitive observations at submm through cm wavelengths…

Principal component analysis (PCA) is one of the most widely used dimension reduction and multivariate statistical techniques. From a probabilistic perspective, PCA seeks a low-dimensional representation of data in the presence of…

We explore the use of principal component analysis (PCA) to characterize high-fidelity simulations and interferometric observations of the millimeter emission that originates near the horizons of accreting black holes. We show…

A spectral principal component analysis (SPCA) of a sample of 87 PG QSOs at $z < 0.5$ is presented for their mid-infrared spectra from Spitzer Space Telescope. We have derived the first five eigenspectra, which account for 85.2\% of the…

Next-generation radio surveys are expected to detect tens of millions of active galactic nuclei (AGN) with a median redshift of z > 1. Beyond targeted surveys, the vast majority of these objects will not have spectroscopic redshifts, whilst…

Principal component analysis is a statistical method, which lowers the number of important variables in a data set. The use of this method for the bursts' spectra and afterglows is discussed in this paper. The analysis indicates that three…

Principal Component Analysis (PCA) is the most widely used tool for linear dimensionality reduction and clustering. Still it is highly sensitive to outliers and does not scale well with respect to the number of data samples. Robust PCA…

We re-examine the technique of determining the redshifts of galaxies from the ratio of the submillimeter-to-radio continuum flux densities based on a recently published catalog of 850~$\mu$m sources. We derived the expected variation of…

We apply principal component analysis (PCA) to a set of electrical output signals from a commercially available superconducting nanowire single-photon detector (SNSPD) to investigate their photon-number-resolving capability. We find that…

Principal Component Analysis (PCA) is a method for estimating a subspace given noisy samples. It is useful in a variety of problems ranging from dimensionality reduction to anomaly detection and the visualization of high dimensional data.…

We develop a Principal Component Analysis aimed at classifying a sub-set of 27,350 spectra of galaxies in the range 0.4 < z < 1.0 collected by the VIMOS Public Extragalactic Redshift Survey (VIPERS). We apply an iterative algorithm to…

Singular value decomposition (SVD) based principal component analysis (PCA) breaks down in the high-dimensional and limited sample size regime below a certain critical eigen-SNR that depends on the dimensionality of the system and the…