Related papers: Automated supervised classification of variable st…
In this poster we present three developments related to the Galactic O-Star Spectroscopic Survey (GOSSS). First, we are making public the first version of MGB, an IDL code that allows the user to compare oberved spectra to a grid of…
We present a new spectroscopic pipeline designed to analyse large numbers of hot massive stars homogeneously. The pipeline has been developed to utilise large grids of FASTWIND non-LTE, line blanketed models in which spherical geometry is…
We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that…
We investigate star-galaxy classification for astronomical surveys in the context of four methods enabling the interpretation of black-box machine learning systems. The first is outputting and exploring the decision boundaries as given by…
One emerging application of machine learning methods is the inference of galaxy cluster masses. In this note, machine learning is used to directly combine five simulated multiwavelength measurements in order to find cluster masses. This is…
Traditional artificial-star tests are widely applied to photometry in crowded stellar fields. However, to obtain reliable binary fractions (and their uncertainties) of remote, dense, and rich star clusters, one needs to recover huge numbers…
We present the fifth part of the OGLE Catalog of Periodic Variable Stars in the Galactic bulge. 574 variable stars: 44 pulsating, 369 eclipsing and 161 miscellaneous type were detected in four fields located symmetrically in galactic…
Temporal and spectral information extracted from a stream of photons received from astronomical sources is the foundation on which we build understanding of various objects and processes in the Universe. Typically astronomers fit a number…
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…
Optical spectroscopic monitoring has been conducted of two O stars in the Small and one in the Large Magellanic Cloud, the spectral characteristics of which place them in the Of?p category, which has been established in the Galaxy to…
We discuss the application of a class of machine learning algorithms known as decision trees to the process of galactic classification. In particular, we explore the application of oblique decision trees induced with different impurity…
Previous calculations of the rates and optical depths due to microlensing only considered resolved stars. However, if a faint unresolved star lens is close enough to a resolved star, the event will be seen by the microlensing experiments…
We used the OGLE-II and OGLE-III photometry of red giants in the Large Magellanic Cloud to select and study objects revealing ellipsoidal variability. We detected 1546 candidates for long period ellipsoidal variables and 121 eclipsing…
Context. In modern astronomy, machine learning has proved to be efficient and effective to mine the big data from the newesttelescopes. Spectral surveys enable us to characterize millions of objects, while long exposure time observations…
The ongoing microlensing searches have generated more photometric measurements of pulsating stars than all previous observing projects combined. In particular, OGLE has made ~ 340,000 B, V, and I-band measurements of ~ 1,300 Cepheids in the…
We present catalog of 26 121 visually inspected eclipsing binary stars identified in the Large Magellanic Cloud during the third phase of the Optical Gravitational Lensing Experiment. The sample is limited to the out-of-eclipse brightness I…
With upcoming all sky surveys such as LSST poised to generate a deep digital movie of the optical sky, variability-based AGN selection will enable the construction of highly-complete catalogs with minimum contamination. In this study, we…
This is the first paper presenting our long-term project aimed at studying the nature of bulges through the analysis of their stellar population gradients. We present deep spectroscopic observations along the minor axis and the data…
We have undertaken quantitative analysis of four LMC and SMC O4--9.7 extreme supergiants using far-ultraviolet FUSE, ultraviolet IUE/HSTand optical VLT UVES spectroscopy. Extended, non-LTE model atmospheres that allow for the consistent…
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…