Related papers: Automated supervised classification of variable st…

Recently, machine learning methods presented a viable solution for automated classification of image-based data in various research fields and business applications. Scientists require a fast and reliable solution to be able to handle the…

The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the…

The Optical Gravitational Lensing Experiment (OGLE) continuously monitors hundreds of thousands of eclipsing binaries in the field of galactic bulge and the Magellanic Clouds. These objects have been classified into main morphological…

A significant degree of misclassification of variable stars through the application of machine learning methods to survey data motivates a search for more reliable and accurate machine learning procedures, especially in light of the very…

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…

Variable stars play a very important role in our understanding of the Milky Way and the universe. In recent years, many survey projects have generated a large amount of photometric data, necessitating classifiers that can quickly identify…

Statistical pattern recognition methods have provided competitive solutions for variable star classification at a relatively low computational cost. In order to perform supervised classification, a set of features is proposed and used to…

The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…

The Optical Gravitational Lensing Experiment (OGLE) is one of the most productive and influential photometric sky surveys in the history of observational astronomy. Originally designed to detect dark matter through gravitational…

Context. The Optical Gravitational Lensing Experiment (OGLE) observed around 450,000 eclipsing binaries (EBs) towards the Galactic Bulge. Decade-long photometric observations such as these provide an exceptional opportunity to thoroughly…

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 both the technical overview and main science drivers of the fourth phase of the Optical Gravitational Lensing Experiment (hereafter OGLE-IV). OGLE-IV is currently one of the largest sky variability surveys worldwide, targeting…

We present a statistical assessment of both, observed and reported, photometric uncertainties in the OGLE-IV Galactic bulge microlensing survey data. This dataset is widely used for the detection of variable stars, transient objects,…

We present on-line, interactive interface to the whole I-band photometry data set obtained in the second phase of the OGLE project (OGLE-II). The raw photometric database is accessed through an additional database using MySQL engine,…

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.…

Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null-hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. Uncorrected…

We present a machine learning package for the classification of periodic variable stars. Our package is intended to be general: it can classify any single band optical light curve comprising at least a few tens of observations covering…

We present an analysis of 991 heartbeat stars (HBSs) from the OGLE Collection of Variable Stars (OCVS). The sample consists of 512 objects located toward the Galactic bulge (GB), 439 in the Large Magellanic Cloud (LMC) and 40 in the Small…

In this experiment, we created a Multiple-Input Neural Network, consisting of Convolutional and Multi-layer Neural Networks. With this setup the selected highest-performing neural network was able to distinguish variable stars based on the…

We present the first edition of a catalog of variable stars from OGLE-II Galactic Bulge data covering 3 years: 1997-1999. Typically 200-300 I band data points are available in 49 fields between -11 and 11 degrees in galactic longitude,…