Related papers: NGC 7097: the AGN and its mirror, revealed by PCA …

With the development of modern technologies such as IFUs, it is possible to obtain data cubes in which one produces images with spectral resolution. To extract information from them can be quite complex, and hence the development of new…

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…

Most massive galaxies show emission lines that can be characterized as LINERs. To what extent this emission is related to AGNs or to stellar processes is still an open question. In this paper, we analysed a sample of such galaxies to study…

In this work, we present an analysis of a data cube obtained with the instrument IFU/GMOS Gemini North telescope centered on the nuclear region of the LINER galaxy NGC 4579. This galaxy is known to have a type 1 AGN (see Eracleous et al.…

Recent developments in (sub-)millimeter facilities have drastically changed the amount of information obtained from extragalactic spectral scans. In this paper, we present a feature extraction technique using principal component analysis…

We apply Principal Component Analysis (PCA) to study the variability of the X-ray continuum in the Seyfert 1 galaxy NGC 7469. The PCA technique is used to separate out linear components contributing to variability between multiple datasets;…

We present an algorithm using Principal Component Analysis (PCA) to subtract galaxies from imaging data, and also two algorithms to find strong, galaxy-scale gravitational lenses in the resulting residual image. The combined method is…

Principal 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…

Understanding the morphology of galaxies is a critical aspect of astrophysics research, providing insight into the formation, evolution, and physical properties of these vast cosmic structures. Various observational and computational…

We apply principal component analysis (PCA) to the integrated intensity maps of 13 molecular lines of the nearby type-2 Seyfert galaxy NGC 1068 obtained by Atacama Large Millimeter/sub-millimeter Array (ALMA) to objectively visualize the…

Principal Component Analysis (PCA) is a well-known technique used to decorrelate a set of vectors. It has been applied to explore the star formation history of galaxies or to determine distances of mass-lossing stars. Here we apply PCA to…

Important but rare and subtle processes driving galaxy morphology and star-formation may be missed by traditional spiral, elliptical, irregular or S\'ersic bulge/disk classifications. To overcome this limitation, we use a principal…

We analyse 400 ks of XMM-Newton data on the active galactic nucleus NGC 1365 using principal component analysis (PCA) to identify model independent spectral components. We find two significant components and demonstrate that they are…

Principal Component Analysis (PCA) is a workhorse of modern data science. While PCA assumes the data conforms to Euclidean geometry, for specific data types, such as hierarchical and cyclic data structures, other spaces are more…

Principal Component Analysis (PCA) is a commonly used tool for dimension reduction in analyzing high dimensional data; Multilinear Principal Component Analysis (MPCA) has the potential to serve the similar function for analyzing tensor…

Principal Component Analysis (PCA) is one of the most important methods to handle high dimensional data. However, most of the studies on PCA aim to minimize the loss after projection, which usually measures the Euclidean distance, though in…

We present a new framework for multi-line analysis that combines kernel principal component analysis (Kernel PCA), an unsupervised machine-learning method, and Kernel SHapley Additive exPlanations (Kernel SHAP), an explainable artificial…

Principal Component Analysis (PCA) is a highly useful topic within an introductory Linear Algebra course, especially since it can be used to incorporate a number of applied projects. This method represents an essential application and…

We present the first principal component analysis (PCA) applied to a sample of 119 Spitzer Infrared Spectrograph (IRS) spectra of local ultraluminous infrared galaxies (ULIRGs) at z<0.35. The purpose of this study is to objectively and…

We use Principal Component Analysis (PCA) to study the gas dynamics in numerical simulations of typical MCs. Our simulations account for the non-isothermal nature of the gas and include a simplified treatment of the time-dependent gas…