Related papers: Variable star classification with a Multiple-Input…
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…
Upcoming synoptic surveys are set to generate an unprecedented amount of data. This requires an automatic framework that can quickly and efficiently provide classification labels for several new object classification challenges. Using data…
The eighth part of the OGLE-III Catalog of Variable Stars (OIII-CVS) contains type II Cepheids in the Small Magellanic Cloud (SMC). The sample consists of 43 objects, including 17 BL Her, 17 W Vir and 9 RV Tau stars (first examples ever…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
We present the first part of a new catalog of variable stars (OIII-CVS) compiled from the data collected in the course of the third phase of the Optical Gravitational Lensing Experiment (OGLE-III). In this paper we describe the catalog of…
In this paper, we present a deep learning system approach to estimating luminosity, effective temperature, and surface gravity of O-type stars using the optical region of the stellar spectra. In previous work, we compare a set of machine…
With the advent of surveys generating multi-epoch photometry and the discovery of large numbers of variable stars, the classification of these stars has to be automatic. We have developed such a classification procedure for about 1700 stars…
The light curves of Cepheids and other variable stars in Field A of IC 1613, obtained with a CCD and no filter ($Wh$ photometry), have been analyzed. It is possible to separate first overtone from fundamental mode population I Cepheids…
We aim to extend and test the classifiers presented in a previous work against an independent dataset. We complement the assessment of the validity of the classifiers by applying them to the set of OGLE light curves treated as variable…
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…
Aims. We introduce a new deep learning tool that estimates stellar parameters (such as effective temperature, surface gravity, and extinction) of young low-mass stars by coupling the Phoenix stellar atmosphere model with a conditional…
Astronomical surveys of celestial sources produce streams of noisy time series measuring flux versus time ("light curves"). Unlike in many other physical domains, however, large (and source-specific) temporal gaps in data arise naturally…
We have analysed Optical Gravitational Lensing Experiment photometry for first overtone classical Cepheids in the Large and Small Magellanic Clouds in search for additional periodicities beyond radial modes. We have used standard…
This project is a massive near-infrared (NIR) search for variable stars in highly reddened and obscured open cluster (OC) fields projected on regions of the Galactic bulge and disk. The search is performed using photometric NIR data in the…
The seventh part of the OGLE-III Catalog of Variable Stars (OIII-CVS) consists of 4630 classical Cepheids in the Small Magellanic Cloud (SMC). The sample includes 2626 fundamental-mode (F), 1644 first-overtone (1O), 83 second-overtone (2O),…
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 new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…
Classifying variable stars is key for understanding stellar evolution and galactic dynamics. With the demands of large astronomical surveys, machine learning models, especially attention-based neural networks, have become the…
The stellar evolution theory of massive stars remains uncalibrated with high-precision photometric observational data mainly due to a small number of luminous stars that are monitored from space. Automated all-sky surveys have revealed…
The first results are presented of a four-year program dedicated to the CCD observations of Cepheids in the nearby galaxy IC 1613. Since the program was carried out with a relatively small telescope, the Dutch 0.9 m at ESO-La Silla, the…