Related papers: Morphological classification of eclipsing binary s…
We present an image classification algorithm using deep learning convolutional neural network architecture, which classifies the morphologies of eclipsing binary systems based on their light curves. The algorithm trains the machine with…
We focus on the automated classification of eclipsing binary stars using deep learning methods to handle the vast data generated by large-scale photometric sky surveys. These surveys produce extensive datasets that are impractical for…
In the last couple of decades, tremendous progress has been achieved in developing robotic telescopes and, as a result, sky surveys (both terrestrial and space) have become the source of a substantial amount of new observational data. These…
We present an automated classification of 2165 \textit{Kepler} eclipsing binary (EB) light curves that accompanied the second \textit{Kepler} data release. The light curves are classified using Locally Linear Embedding, a general nonlinear…
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…
Eclipsing binaries are crucial astrophysical laboratories for studying stellar parameters and evolutionary processes. In this study, we constructed a machine-learning-based model for systematic phenomenological classification of eclipsing…
In this work we present a system for the automatic classification of the light curves of eclipsing binaries. This system is based on a classification scheme that aims to separate eclipsing binary sistems according to their geometrical…
We present a classification of the light curve morphologies of eclipsing binary systems observed by ASAS-SN based on their light curve images. The data of 16500 eclipsing systems having three different classes (detached Algol type, $\beta$…
We describe a new neural-net based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general…
This study presents a multi-task machine learning framework for simultaneous morphology classification and physical parameter estimation of eclipsing binaries using photometric light curves. We train Random Forest and XGBoost ensemble…
Spotted stars in eclipsing binary systems allow us to gather significant information about the stellar surface inhomogeneities that is otherwise impossible from only photometric data. Starspots can be scanned using the eclipse (or transit)…
We have developed a procedure for the classification of eclipsing binaries from their light-curve parameters and spectral type. The procedure was tested on more than 1000 systems with known classification, and its efficiency was estimated…
Eclipsing binary systems (EBs), as foundational objects in stellar astrophysics, have garnered significant attention in recent years. These systems exhibit periodic decreases in light intensity when one star obscures the other from the…
The primary aim of this research is to evaluate several convolutional neural network-based object detection algorithms for identifying oscillation-like patterns in light curves of eclipsing binaries. This involves creating a robust…
Achieving maximum scientific results from the overwhelming volume of astronomical data to be acquired over the next few decades will demand novel, fully automatic methods of data analysis. Artificial intelligence approaches hold great…
In-depth analysis of eclipsing binary (EB) observational data collected for several decades can inform us about a lot of astrophysically interesting processes taking place in the systems. We have developed a wide-ranging method for the…
Recent studies indicate that the physical properties of eclipsing binaries can be extracted from the derivatives of their light curves. A classification scheme for the derivatives of light curves would be helpful for identifying key…
We present a new algorithm -- Eclipsing Binary Automated Solver (EBAS), to analyse lightcurves of eclipsing binaries. The algorithm is designed to analyse large numbers of lightcurves, and is therefore based on the relatively fast EBOP…
Large surveys producing tera- and petabyte-scale databases require machine-learning and knowledge discovery methods to deal with the overwhelming quantity of data and the difficulties of extracting concise, meaningful information with…
We present a detailed V-band photometric light curve modeling of 30 eclipsing binaries using the data from Pietrukowicz et al. (2009) collected with the European Southern Observatory Very Large Telescope (ESO VLT) of diameter 8-m. The light…