Related papers: Automatic classification of K2 pulsating stars usi…
We report the results of an observational study aimed at searching for magnetic pulsating hot stars suitable for magneto-asteroseismology. A sample of sixteen chemically peculiar stars was selected and analysed using both high-resolution…
We have investigated and applied machine-learning algorithms for Infrared Colour Selection of Galactic Wolf-Rayet (WR) candidates. Objects taken from the GLIMPSE catalogue of the infrared objects in the Galactic plane can be classified into…
The K20 survey is a near infrared-selected, deep (Ks < 20) redshift survey targeting galaxies in two independent regions of the sky, the CDFS and the q0055-2659 field. The total Ks-selected sample includes 545 objects. Optical spectra for…
With only two functional reaction wheels, Kepler cannot maintain stable pointing at its original target field and entered a new mode of observation called K2. Our method is based on many years of experience in planet hunting for the CoRoT…
We describe an Artificial Neural Network (ANN) approach to classification of galaxy images and spectra. ANNs can replicate the classification of galaxy images by a human expert to the same degree of agreement as that between two human…
The Kepler K2 mission now makes it possible to find and study a wider variety of eclipsing binary stars than has been possible to-date, particularly long-period systems with narrow eclipses. Our aim is to characterise eclipsing binary stars…
Some pulsating stars are good clocks. When they are found in binary stars, the frequencies of their luminosity variations are modulated by the Doppler effect caused by orbital motion. For each pulsation frequency this manifests itself as a…
Very high precision seismic space missions such as CoRoT and Kepler provide the means for testing the modelling of transport processes in stellar interiors. For some stars, such as $\delta$ Scuti $\gamma$ Doradus and Be stars, for instance,…
The Kepler Mission began its 3.5-year photometric monitoring campaign in May 2009 on a select group of approximately 150,000 stars. The stars were chosen from the ~half million in the field of view that are brighter than 16th magnitude. The…
Analyses of stellar spectra often begin with the determination of a number of parameters that define a model atmosphere. This work presents a prototype for an automated spectral classification system that uses a 15 nm-wide region around…
The space-missions MOST, CoRoT, and Kepler deliver a huge amount of high-quality photometric data suitable to study numerous pulsating stars. Our ultimate goal is a detection and analysis of an extended sample of Gamma Dor-type pulsating…
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 have developed a new method, K2, optimized for the detection of galaxy clusters in multicolor images. Based on the Red Sequence approach, K2 detects clusters using simultaneous enhancements in both colors and position. The detection…
The present study reports the discovery of Sun-like stars, namely main-sequence stars with $T_{\rm eff}$, $\log g$ and rotation periods $P_{rot}$ similar to solar values, presenting evidence of surface differential rotation. An…
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…
We analyze light curves of 284,834 unique K2 targets using a Gaussian process model with a quasi-periodic kernel function. By crossmatching K2 stars to observations from Gaia Data Release 2, we have identified 69,627 likely main-sequence…
We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that…
Most existing star-galaxy classifiers depend on the reduced information from catalogs, necessitating careful data processing and feature extraction. In this study, we employ a supervised machine learning method (GoogLeNet) to automatically…
We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in…
Given multiband photometric data from the SDSS DR6, we estimate galaxy redshifts. We employ a Random Forest trained on color features and spectroscopic redshifts from 80,000 randomly chosen primary galaxies yielding a mapping from color to…