Related papers: Automated supervised classification of variable st…

Log-based predictive maintenance of computing centers is a main concern regarding the worldwide computing grid that supports the CERN (European Organization for Nuclear Research) physics experiments. A log, as event-oriented adhoc…

A direct approach to studying the galaxy-halo connection is to analyze groups and clusters of galaxies that trace the underlying dark matter halos, emphasizing the importance of identifying galaxy clusters and their associated brightest…

We present a study of the Oosterhoff (Oo) dichotomy in the Galactic bulge using 8\,141 fundamental mode RR~Lyrae stars. We used public photometric data from the Optical Gravitational Lensing Experiment (OGLE) and the Vista Variables in the…

We present 20 newly discovered candidates for binary systems with an RR~Lyrae companion. Using the photometric data from the Optical Gravitational Lensing Experiment (OGLE) and Korea Microlensing Telescope Network (KMTNet) for the Galactic…

Context: The huge and still rapidly growing amount of galaxies in modern sky surveys raises the need of an automated and objective classification method. Unsupervised learning algorithms are of particular interest, since they discover…

Pulsating variable stars can be powerful tools to study the structure, formation and evolution of galaxies. I discuss the role that the Magellanic Clouds' pulsating variables play in our understanding of the whole Magellanic System, in…

We have developed a fully-automated pipeline for systematically identifying and analyzing eclipsing binaries within large datasets of light curves. The pipeline is made up of multiple tiers which subject the light curves to increasing…

We describe a first attempt to apply adaptive optics to the study of resolved stellar populations in galaxies. Advantages over traditional approaches are (i) improved spatial resolution and point-source sensitivity through adaptive optics,…

The automatic classification of X-ray detections is a necessary step in extracting astrophysical information from compiled catalogs of astrophysical sources. Classification is useful for the study of individual objects, statistics for…

In this work we present combined optical and X-ray cluster detection methods in an area near the North Galactic Pole area, previously covered by the SDSS and 2dF optical surveys. The same area has been covered by shallow ($\sim 1.8$…

The analysis of the variability of active galactic nuclei (AGNs) at different wavelengths and the study of possible correlations among different spectral windows are nowadays a major field of inquiry. Optical variability has been largely…

This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…

In this paper we introduce the SEAGLE (i.e. Simulating EAGLE LEnses) program, that approaches the study of galaxy formation through strong gravitational lensing, using a suite of high-resolution hydrodynamic simulations, Evolution and…

The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. To process this information and to extract all possible knowledge, machine learning…

In the past years we have made great efforts to reduce the statistical and systematic uncertainties in stellar parameter and chemical abundance determinations of early B-type stars. Both the construction of robust model atoms for non-LTE…

Classifying galaxies is an essential step for studying their structures and dynamics. Using GalaxyZoo2 (GZ2) fractions thresholds, we collect 545 and 11,735 samples in non-galaxy and galaxy classes, respectively. We compute the Zernike…

We have obtained HST and ground-based observations of a sample of 20 O-type stars in the LMC and SMC, including six of the hottest massive stars known (subtypes O2-3) in the R136 cluster. In general, these data include (a) the HST UV…

While microlensing is very rare, occurring on average once per million stars observed, current and near-future surveys are coming online with the capability of providing photometry of almost the entire visible sky to depths up to R ~ 22 mag…

Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and…

Our multi-view metric learning framework enables robust characterization of star categories by directly learning to discriminate in a multi-faceted feature space, thus, eliminating the need to combine feature representations prior to…