Related papers: Structural properties and classification of variab…

Variable stars have been used for over one hundred years as probes for determining astronomical distances; these distances can be used to map the three-dimensional (3D) structure of nearby galaxies. Exploiting the effect that moving to the…

We present preliminary results of the generalized Principal Component Analysis (PCA) of light curves of 82 magnetic chemically peculiar (further mCP) stars applied to 54 thousand individual photometric observations in the uvby and Hp…

We present results of star variability analysis in OGLE II first bulge field. Photometric database was derived by means of image subtraction method (Wozniak 2000) and contains 4597 objects pre-classified as variables. We analyzed all the…

Principal components analysis (PCA) is a classical method for the reduction of dimensionality of data in the form of n observations (or cases) of a vector with p variables. For a simple model of factor analysis type, it is proved that…

Independent component analysis (ICA) is now a widely used solution for the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. Independent vector analysis (IVA) generalizes ICA to multiple datasets, i.e., to…

We report on the automated classification of Hipparcos variable stars by a supervised classification algorithm known as Support Vector Machines. The dataset comprised about 3200 stars, each characterized by 51 features. These are the B-V…

Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify…

We introduce coroICA, confounding-robust independent component analysis, a novel ICA algorithm which decomposes linearly mixed multivariate observations into independent components that are corrupted (and rendered dependent) by hidden…

We present the first integrated light, TESS-based light curves for star clusters in the Milky Way, Small Magellanic Cloud, and Large Magellanic Cloud. We explore the information encoded in these light curves, with particular emphasis on…

Context. Discovery of new variability classes in large surveys using multivariate statistics techniques such as clustering, relies heavily on the correct understanding of the distribution of known classes as point processes in parameter…

High-resolution spectroscopic measurements of OB stars are important for understanding processes like stellar evolution, but require labor-intensive observations. In contrast, photometric missions like the Transiting Exoplanet Survey…

In this work we study the relevance of the component separation technique based on the Independent Component Analysis (ICA) and investigate its performance in the context of a limited sky coverage observation and from the viewpoint of our…

This work addresses a procedure to estimate fundamental stellar parameters such as T eff , logg, [Fe/H], and v sin i using a dimensionality reduction technique called Principal Component Analysis (PCA), applied to a large database of…

The Galactic center (GC) is the densest region of the Milky Way. Variability surveys towards the GC potentially provide the largest number of variable stars per square degree within the Galaxy. However, high stellar density is also a…

This project outlines the complete development of a variable star classification algorithm methodology. With the advent of Big-Data in astronomy, professional astronomers are left with the problem of how to manage large amounts of data, and…

Principal component analysis (PCA) is arguably the most widely used approach for large-dimensional factor analysis. While it is effective when the factors are sufficiently strong, it can be inconsistent when the factors are weak and/or the…

This work is part of an effort to detect secular variable objects in large scale surveys by analysing their path in color-magnitude diagrams. To this aim, we first present the variability morphologies in the V/V-I diagram of several types…

Modern astronomical surveys produce millions of light curves of variable sources. These massive data sets challenge the community to create automatic light-curve processing methods for detection, classification, and characterisation of…

Classical Cepheid and RR Lyrae variables are fundamental tracers of cosmic distances and stellar evolution and pulsation. Light curve analysis and pulsation properties of these radially pulsating stars provide stringent tests for…

Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches. To this end, we use Classification Without Labels…