Related papers: A study of supervised classification of Hipparcos …

We present $V$-band light curves of 61 variables from the core of the globular cluster M5 obtained using a newly developed image subtraction method (ISM). Four of these variables were previously unknown. Only 26 variables were found in the…

The luminosity changes of most types of variable stars are correlated in the different wavelengths, and these correlations may be exploited for several purposes: for variability detection, for distinction of microvariability from noise, for…

Recent CCD observations were performed in the period 1998-2004 for a large sample of visual double and multiple stars selected from the Hipparcos Catalogue and/or from the Gliese Catalogue of Nearby Stars. Accurate astrometric and…

Classifying variable stars is crucial for advancing our understanding of stellar evolution and dynamics. As large-scale surveys generate increasing volumes of light curve data, the demand for automated and reliable classification techniques…

The importance of using fast and automatic methods to classify variable stars for large amounts of data is undeniable. There have been many attempts to classify variable stars by traditional algorithms like Random Forest. In recent years,…

We develop a Principal Component Analysis aimed at classifying a sub-set of 27,350 spectra of galaxies in the range 0.4 < z < 1.0 collected by the VIMOS Public Extragalactic Redshift Survey (VIPERS). We apply an iterative algorithm to…

Data collection often results in records that have missing values or variables. This investigation compares 3 different data imputation models and identifies their merits by using accuracy measures. Autoencoder Neural Networks, Principal…

We present a new QSO selection algorithm using a Support Vector Machine (SVM), a supervised classification method, on a set of extracted times series features including period, amplitude, color, and autocorrelation value. We train a model…

This paper describes the first part of the photometric data from the 9x9 deg ASAS cameras monitoring the whole southern hemisphere in V-band. Data acquisition and reduction pipeline is described and preliminary list of variable stars is…

The magnetic chemically peculiar (CP2) stars of the upper main sequence are well-suited for investigating the impact of magnetic fields on the surface layers of stars, which leads to abundance inhomogeneities (spots) resulting in…

We present VIVACE, the VIrac VAriable Classification Ensemble, a catalogue of variable stars extracted from an automated classification pipeline for the Vista Variables in the V\'ia L\'actea (VVV) infrared survey of the Galactic bar/bulge…

The variable stars in the VSX catalog are derived from a multitude of inhomogeneous data sources and classification tools. This inhomogeneity complicates our understanding of variable star types, statistics, and properties, and it directly…

During the last decade, a considerable amount of effort has been made to classify variable stars using different machine learning techniques. Typically, light curves are represented as vectors of statistical descriptors or features that are…

We have monitored a 3 deg2 area toward Serpens Main in the Pan-STARRS1 r, i, and z bands from 2016 April to September. Light curves of more than 11,000 stars in each band were obtained, and 143 variables have been identified. Among those,…

The success of automatic classification of variable stars strongly depends on the lightcurve representation. Usually, lightcurves are represented as a vector of many statistical descriptors designed by astronomers called features. These…

We develop Bayesian statistical methods for discovering and assigning probabilities to non-random (e.g., physical) stellar companions. These companions are either presently bound or were previously bound. The probabilities depend on…

Hot sub-luminous stars represent a population of stripped and evolved red giants that is located on the extreme horizontal branch. Since they exhibit a wide range of variability due to pulsations or binary interactions, it is crucial to…

The advancement in the field of data science especially in machine learning along with vast databases of variable star projects like the Optical Gravitational Lensing Experiment (OGLE) encourages researchers to analyse as well as classify…

During the last ten years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric datasets…

We present the first analysis of results from the SuperWASP Variable Stars Zooniverse project, which is aiming to classify 1.6 million phase-folded light curves of candidate stellar variables observed by the SuperWASP all sky survey with…