Related papers: Structural properties and classification of variab…

With the availability of large-scale surveys like Kepler and TESS, there is a pressing need for automated methods to classify light curves according to known classes of variable stars. We introduce a new algorithm for classifying light…

We apply three data science techniques, Nonnegative Matrix Factorization (NMF), Principal Component Analysis (PCA) and Independent Component Analysis (ICA), to simulated X-ray energy spectra of a particular class of super-massive black…

BVI photometry of the Magellanic Clouds collected during the OGLE-II microlensing experiment makes it possible to study in detail photometric properties of the "major" stellar distance indicators in the Magellanic Clouds. In addition to…

The period of pulsation and the structure of the light curve for Cepheid and RR Lyrae variables depend on the fundamental parameters of the star: mass, radius, luminosity, and effective temperature. Here we train artificial neural networks…

Massive stars play a fundamental role in galactic evolution through their strong stellar winds, chemical enrichment, and feedback mechanisms. Accurate modelling of their atmospheres and winds is critical for understanding their physical…

In the second part of the OGLE-III Catalog of Variable Stars (OIII-CVS) we present 197 type II Cepheids and 83 anomalous Cepheids in the Large Magellanic Cloud (LMC). The sample of type II Cepheids consists of 64 BL Her stars, 96 W Vir…

This project is a massive near-infrared (NIR) search for variable stars in highly reddened and obscured open cluster (OC) fields projected on regions of the Galactic bulge and disk. The search is performed using photometric NIR data in the…

We present results from a detailed analysis of theoretical and observed light curves of classical Cepheid variables in the Galaxy and the Magellanic Clouds. Theoretical light curves of Cepheid variables are based on non-linear convective…

Principal Component Analysis (PCA)-based techniques can separate data into different uncorrelated components and facilitate the statistical analysis as a pre-processing step. Independent Component Analysis (ICA) can separate statistically…

Independent component analysis (ICA) is linked up with the problem of estimating a non linear functional of a density, for which optimal estimators are well known. The precision of ICA is analyzed from the viewpoint of functional spaces in…

Nonlinear ICA is a fundamental problem for unsupervised representation learning, emphasizing the capacity to recover the underlying latent variables generating the data (i.e., identifiability). Recently, the very first identifiability…

Insights on stellar surface large-scale magnetic field topologies are usually drawn by applying Zeeman-Doppler-Imaging (ZDI) to the observed spectropolarimetric time series. However, ZDI requires experience for reliable results to be…

Within the last years, the classification of variable stars with Machine Learning has become a mainstream area of research. Recently, visualization of time series is attracting more attention in data science as a tool to visually help…

High-resolution spectroscopy of strong chromospheric absorption lines delivers nowadays several millions of spectra per observing day, when using fast scanning devices to cover large regions on the solar surface. Therefore, fast and robust…

Recent work on Ultra Long Period Cepheids (ULPCs) has suggested their usefulness as a distance indicator, but has not commented on their relationship as compared with other types of variable stars. In this work, we use Fourier analysis to…

Ultralow amplitude (ULA) and strange mode Cepheids are thought to be pulsating variable stars that are near to or are at the edges of the classical instability strip. Until now, a few dozen such variable star candidates have been found both…

Application of independent component analysis (ICA) as an unmixing and image clustering technique for high spatial resolution Raman maps is reported. A hyperspectral map of a fixed human cell was collected by a Raman micro spectrometer in a…

We present a new analysis of the long-period variables in the Large Magellanic Cloud (LMC) from the MACHO Variable Star Catalog. Three-quarters of our sample of evolved, variable stars have periodic light curves. We characterize the stars…

Independent Component Analysis (ICA) is an effective unsupervised tool to learn statistically independent representation. However, ICA is not only sensitive to whitening but also difficult to learn an over-complete basis. Consequently, ICA…

In this paper, we employe a new statistical analysis technique, Ensemble Learning for Independent Component Analysis (EL-ICA), on the synthetic galaxy spectra from a newly released high resolution evolutionary model by Bruzual & Charlot. We…