Related papers: Structural properties and classification of variab…
Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning architectures such as convolutional neural networks,…
Independent component analysis (ICA) is a fundamental data processing technique to decompose the captured signals into as independent as possible components. Computing the contrast function, which serves as a measure of independence of…
Independent component analysis (ICA) is a widely used method in various applications of signal processing and feature extraction. It extends principal component analysis (PCA) and can extract important and complicated components with small…
The IACOB and OWN surveys are two ambitious complementary observational projects which have made available a large multi-epoch spectroscopic database of optical high resolution spectra of Galactic massive O-type stars. As a first step in…
In this work, we discuss the determination of the distance to the Large Magellanic Cloud (LMC) using the Leavitt Law, utilizing the public catalog of Classical Cepheid Variable stars from the observational project OGLE-IV (The Optical…
In a previous paper, we studied two statistical methods used to analyse the variability of active galactic nuclei (AGNs): the C and F statistics. Applying them to observed differential light-curves of 39 AGNs, we found that, even though the…
In the independent component model, the multivariate data is assumed to be a mixture of mutually independent latent components, and in independent component analysis (ICA) the aim is to estimate these latent components. In this paper we…
We analyse the theoretical light curves of Cepheid variables at optical ({\it UBVRI}) and near-infrared ({\it JKL}) wavelengths using the Fourier decomposition and principal component analysis methods. The Cepheid light curves are based on…
We comprehensively study the variability of Miras in the Large Magellanic Cloud (LMC) by simultaneous analysing light curves in 14 bands in the range of 0.5$-$24 microns. We model over 20-years-long, high cadence $I$-band light curves…
We present the first application of data-driven techniques for dynamical system analysis based on Koopman theory to variable stars. We focus on light curves of RRLyrae type variables, in the Galactic globular cluster $\omega$ Centauri.…
We present the results of the light curve model fitting technique applied to optical and near-infrared photometric data for a sample of 18 Classical Cepheids (11 fundamentals and 7 first overtones) in the Large Magellanic Cloud (LMC). We…
Optical Coherence Tomography (OCT) is an emerging technique in the field of biomedical imaging, with applications in ophthalmology, dermatology, coronary imaging etc. OCT images usually suffer from a granular pattern, called speckle noise,…
During the last decade, considerable effort has been made to perform automatic classification of variable stars using machine learning techniques. Traditionally, light curves are represented as a vector of descriptors or features used as…
We have developed a web tool to perform Principal Component Analysis (PCA, Murtagh & Heck 1987; Kendall 1980) onto spectral data. The method is especially designed to perform spectral classification of galaxies from a sample of input…
We present the OGLE collection of delta Scuti stars in the Large Magellanic Cloud and in its foreground. Our dataset encompasses a total of 15 256 objects, constituting the largest sample of extragalactic delta Sct stars published so far.…
The Independent Component Analysis (ICA) algorithm is implemented as a neural network for separating signals of different origin in astrophysical sky maps. Due to its self-organizing capability, it works without prior assumptions on the…
Independent component analysis (ICA) is a statistical method for transforming an observable multidimensional random vector into components that are as statistically independent as possible from each other.Usually the ICA framework assumes a…
We investigate the infrared variability of carbon stars in the Large Magellanic Cloud (LMC). Our sample consists of 11,134 carbon stars identified in both visual and infrared bands. Among these, 1,184 objects are known Mira variables based…
With the advent of surveys generating multi-epoch photometry and the discovery of large numbers of variable stars, the classification of these stars has to be automatic. We have developed such a classification procedure for about 1700 stars…
Independent component analysis (ICA) is a computational method for separating a multivariate signal into subcomponents assuming the mutual statistical independence of the non-Gaussian source signals. The classical Independent Components…