Related papers: Automated supervised classification of variable st…
The creation of a 3D map of the bulge using RRLyrae (RRL) is one of the main goals of the VVV(X) surveys. The overwhelming number of sources under analysis request the use of automatic procedures. In this context, previous works introduced…
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…
The first step when investigating time varying data is the detection of any reliable changes in star brightness. This step is crucial to decreasing the processing time by reducing the number of sources processed in later, slower steps.…
Vast amounts of astronomical photometric data are generated from various projects, requiring significant effort to identify variable stars and other object classes. In light of this, a general, widely applicable classification framework…
Machine-learning (ML) algorithms will play a crucial role in studying the large datasets delivered by new facilities over the next decade and beyond. Here, we investigate the capabilities and limits of such methods in finding galaxies with…
Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning architectures such as convolutional neural networks,…
We describe a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases. This will…
In the era of rapidly increasing amounts of time series data, classification of variable objects has become the main objective of time-domain astronomy. Classification of irregularly sampled time series is particularly difficult because the…
We present the catalog of 2580 eclipsing binary stars detected in 4.6 square degree area of the central parts of the Large Magellanic Cloud. The photometric data were collected during the second phase of the OGLE microlensing search from…
The light curves of the OGLE microlensing candidates have been reconstructed using the image subtraction method. A large improvement of the photometric accuracy has been found in comparison with previous processing of the data with DoPHOT.…
We present here a new major part of the OGLE Collection of Variable Stars - OGLE Collection of Galactic Cepheids. The new dataset was extracted from the Galaxy Variability Survey images - a dedicated large-scale survey of the Galactic disk…
Dusty stellar point sources are a significant stage in stellar evolution and contribute to the metal enrichment of galaxies. These objects can be classified using photometric and spectroscopic observations with color-magnitude diagrams…
Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying…
We propose a new method to investigate the coefficient of the selective extinction, based on two band photometry. This method uses red clump stars as a means to construct the reddening curve. We apply this method to the OGLE color-magnitude…
With the advent of large spectroscopic surveys, automated stellar parameter determination has become commonplace. Nevertheless, spectral classification still offers a quick and useful alternative for obtaining parameter estimates for large…
Long secondary periods (LSPs) in luminous red giants remain the only major class of long-period stellar variability without a secure physical origin. Competing hypotheses include binaries with dusty companions and oscillatory convective…
We present a nearly complete collection of type II Cepheids in the Magellanic System. The sample consists of 338 objects: 285 and 53 variables in the Large and Small Magellanic Clouds, respectively. Based on the pulsation periods and…
We develop a straightforward and quantitative two-step method for spectroscopically classifying galaxies from the low signal-to-noise (S/N) optical spectra typical of galaxy redshift surveys. First, using \chi^2-fitting of characteristic…
The Magellanic Clouds (MCs) are excellent locations to study stellar dust emission and its contribution to galaxy evolution. Through spectral and photometric classification, MCs can serve as a unique environment for studying stellar…
On the basis of the Galactic O-Star Spectroscopic Survey (GOSSS), a detailed systematic investigation of the O Vz stars is presented. The currently used spectral classification criteria are rediscussed, and the Vz phenomenon is recalibrated…