Related papers: Automatic classification of eclipsing binary stars…
In this paper, we revisit the problem of clustering 1318 new variable stars found in the Milky way. Our recent work distinguishes these stars based on their light curves which are univariate series of brightness from the stars observed at…
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…
In the new era of large-scale astronomical surveys, automated methods of analysis and classification of bulk data are a fundamental tool for fast and efficient production of deliverables. This becomes ever more imminent as we enter the Gaia…
We introduce a special class of functions for mathematical modeling of periodic signals of special shape with irregularly spaced arguments. This method was developed for determination of phenomenological characteristics of the light curves,…
There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a…
In this paper we present a catalog of 4584 eclipsing binaries observed during the first two years (26 sectors) of the TESS survey. We discuss selection criteria for eclipsing binary candidates, detection of hither-to unknown eclipsing…
We present a novel automated methodology to detect and classify periodic variable stars in a large database of photometric time series. The methods are based on multivariate Bayesian statistics and use a multi-stage approach. We applied our…
We would like to investigate the information contained in our observations and to what extent each of them contributes individually to constraining the physical parameters of the system we are investigating. To do this, we present a study…
Spectroscopy represents the ideal observational method to maximally extract information from galaxies regarding their star formation and chemical enrichment histories. However, absorption spectra of galaxies prove rather challenging at high…
Detached eclipsing binary star systems are our primary source of measured physical properties of normal stars. I introduce DEBCat: a catalogue of detached eclipsing binaries with mass and radius measurements to the 2% precision necessary to…
Lensed quasars are key to many areas of study in astronomy, offering a unique probe into the intermediate and far universe. However, finding lensed quasars has proved difficult despite significant efforts from large collaborations. These…
We introduce a new machine learning based technique to detect exoplanets using the transit method. Machine learning and deep learning techniques have proven to be broadly applicable in various scientific research areas. We aim to exploit…
Common variable star classifiers are built only with the goal of producing the correct class labels, leaving much of the multi-task capability of deep neural networks unexplored. We present a periodic light curve classifier that combines a…
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 have begun a programme to obtain high-precision photometry of bright detached eclipsing binary (dEB) stars with the Wide field InfraRed Explorer (WIRE) satellite. Due to the small aperture of WIRE only stars brighter than V=6 can be…
Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification…
The orbital inclination of an eclipsing binary is generally determined through light curve analysis. Binary parameters in the light curve analysis are typically constrained through the use of optimization and sampling techniques. We propose…
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 observed light curves of most eclipsing binaries and stars with transiting planets can be well described and interpreted by current advanced physical models which also allow for the determination of many physical parameters of eclipsing…
Most existing star-galaxy classifiers use the reduced summary information from catalogs, requiring careful feature extraction and selection. The latest advances in machine learning that use deep convolutional neural networks allow a machine…