Related papers: Imbalanced learning for RR Lyrae stars
We utilize a complete sample of RR Lyrae stars discovered by the QUEST survey using light curves to design selection criteria based on SDSS colors. Thanks to the sensitivity of the u-g color to surface gravity and of g-r color to effective…
We present a method for selecting RR Lyrae (RRL) stars (or other type of variable stars) in the absence of a large number of multi-epoch data and light curve analyses. Our method uses color and variability selection cuts that are defined by…
RR Lyrae stars are useful chemical tracers thanks to the empirical relationship between their heavy-element abundance and the shape of their light curves. However, the consistent and accurate calibration of this relation across multiple…
Although many globular clusters (GCs) have been identified in the Galaxy, their population is estimated to be incomplete, especially in regions with strong crowding and interstellar extinction such as the Galactic bulge and plane.RR Lyrae…
We have investigated the feasibilities and accuracies of the identifications of RR Lyrae stars and quasars from the simulated data of the Multi-channel Photometric Survey Telescope (Mephisto) W Survey. Based on the variable sources light…
RR Lyrae stars may be the best practical tracers of Galactic halo (sub-)structure and kinematics. The PanSTARRS1 (PS1) $3\pi$ survey offers multi-band, multi-epoch, precise photometry across much of the sky, but a robust identification of…
RR Lyrae stars (RRLs) are old pulsating variables widely used as metallicity tracers due to the correlation between their metal abundances and light curve morphology. With ESA Gaia DR3 providing light curves for about 270,000 RRLs, there is…
Machine learning has achieved an important role in the automatic classification of variable stars, and several classifiers have been proposed over the last decade. These classifiers have achieved impressive performance in several…
One of the major science goals of the SkyMapper Survey of the Southern Hemisphere sky is the determination of the shape and extent of the halo of the Galaxy. In this paper we quantify the likely efficiency and completeness of the survey as…
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…
Aiming to extend the census of RR Lyrae stars to highly reddened low-latitude regions of the central Milky Way, we performed a deep near-IR variability search using data from the VISTA Variables in the V\'ia L\'actea (VVV) survey of the…
We present new statistical parallax solutions for the absolute magnitude and kinematics of RR Lyrae stars. New proper motion, radial velocity, and abundance data are used; the new data set is 50% larger, and of higher quality, than…
We present a three-dimensional analysis of a sample of 22 859 type $ab$ RR Lyrae stars in the Magellanic System from the OGLE-IV Collection of RR Lyrae stars. The distance to each object was calculated based on its photometric metallicity…
Understanding the formation and evolutionary history of the Milky Way requires detailed mapping of its stellar components, which preserve fossil records of the Galaxy's assembly through cosmic time. RR Lyrae stars are particularly…
RR Lyrae stars are standard candles with characteristic photometric variability and serve as powerful tracers of Galactic structure, substructure, accretion history, and dark matter content. Here we report the discovery of distant RR Lyrae…
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 accurate automated classification of variable stars into their respective sub-types is difficult. Machine learning based solutions often fall foul of the imbalanced learning problem, which causes poor generalisation performance in…
With the aid of the All Sky Automated Survey (ASAS) database on the Galactic field, we compare the iron abundances of fundamental mode RR Lyrae stars derived from the Fourier parameters with those obtained from low-dispersion spectroscopy.…
Astronomy is entering an unprecedented era of Big Data science, driven by missions like the ESA's Gaia telescope, which aims to map the Milky Way in three dimensions. Gaia's vast dataset presents a monumental challenge for traditional…
We investigate the ability of machine learning to infer the virial mass ($M_{\rm vir}$) and the scale radius ($r_{\rm s}$) of galaxy clusters from their observables. Using the Uchuu--UniverseMachine galaxy catalog at $z=0.093$, we generate…