相关论文: Diagnosing large-scale stellar magnetic fields usi…

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…

An empirical method of modeling the stellar spectrum of galaxies is proposed, based on two successive applications of Principal Component Analysis (PCA). PCA is first applied to the newly available stellar library STELIB, supplemented by…

Principal Component Analysis (PCA) is being extensively used in Astronomy but not yet exhaustively exploited for variability search. The aim of this work is to investigate the effectiveness of using the PCA as a method to search for…

Observational constraints on stellar magnetic fields are essential to both stellar and planetary physics. Recent studies revealed the diversity and evolution of large-scale magnetic fields in low-mass stars. These large-scale fields only…

Remote sensing observations, products and simulations are fundamental sources of information to monitor our planet and its climate variability. Uncovering the main modes of spatial and temporal variability in Earth data is essential to…

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…

(Abridged) Environmental differences in the stellar populations of early-type galaxies are explored using principal component analysis (PCA), focusing on differences between elliptical galaxies in Hickson Compact Groups (HCGs) and in the…

Using spectropolarimetry, we investigate the large-scale magnetic topologies of stars hosting close-in exoplanets. A small survey of ten stars has been done with the twin instruments TBL/NARVAL and CFHT/ESPaDOnS between 2006 and 2011. Each…

Global magnetic fields of active solar-like stars are nowadays routinely detected with spectropolarimetric measurements and are mapped with Zeeman-Doppler imaging (ZDI). However, due to the cancellation of opposite field polarities,…

Principal component analysis (PCA) is a powerful method that can identify patterns in large, complex data sets by constructing low-dimensional order parameters from higher-dimensional feature vectors. There are increasing efforts to use…

Previous studies have related surface temperature maps, obtained with the Doppler imaging (DI) technique, of LQ Hya with long-term photometry. We compare surface magnetic field maps, obtained with the Zeeman Doppler imaging (ZDI) technique,…

Historically, the magnetic field geometries of the chemically peculiar Ap stars were modelled in the context of a simple dipole field. However, with the acquisition of increasingly sophisticated diagnostic data, it has become clear that the…

We present a new straightforward principal component analysis (PCA) method based on the diagonalization of the weighted variance-covariance matrix through two spectral decomposition methods: power iteration and Rayleigh quotient iteration.…

Stellar Magnetism affects all spectral types and exists and varies throughout the evolution of stars. Magnetic fields can affect not only the interior of stars, but also their circumstellar environments. In this chapter, we concentrate on…

Low-mass stars are known to have magnetic fields that are believed to be of dynamo origin. Two complementary techniques are principally used to characterise them. Zeeman-Doppler imaging (ZDI) can determine the geometry of the large-scale…

We present a technique that combines Zeeman Doppler imaging (ZDI) principles with a potential field mapping prescription in order to gain more information about the surface field topology of rapid rotators. This technique is an improvement…

We consider estimation of large approximate factor models in high-dimensional panels of stationary time series using Principal Component Analysis (PCA). We review the key results establishing the necessary and sufficient conditions for…

We present new magnetic field measurements of the K2 main-sequence star $\epsilon$ Eridani based on principal components analysis (PCA) line-profile reconstructions. The aim of this paper is to quantify the surface-averaged magnetic field…

We describe techniques to characterise the light-curves of regular variable stars by applying principal component analysis (PCA) to a training set of high quality data, and to fit the resulting light-curve templates to sparse and noisy…

Principal component analysis (PCA) is a widely used method for data processing, such as for dimension reduction and visualization. Standard PCA is known to be sensitive to outliers, and thus, various robust PCA methods have been proposed.…