Related papers: The VMC Survey : LI. Classifying extragalactic sou…

We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…

The Fermi Gamma-ray Space Telescope is producing the most detailed inventory of the gamma-ray sky to date. Despite tremendous achievements approximately 25% of all Fermi extragalactic sources in the Second Fermi LAT Catalogue (2FGL) are…

We made 22 observations on the Small Magellanic Cloud (SMC) and covered full regions by the end of the ASCA mission. We detected 106 discrete sources with a criterion of S/N > 5 and performed systematic analyses on all of the sources. We…

The joint catalogue of Active Galactic Nuclei selected from optical identifications of X-ray sources was created as a combination of two samples: Hamburg-ROSAT Catalogue (HRC) and Byurakan-Hamburg-ROSAT Catalogue (BHRC). Both are based on…

The Large Magellanic Cloud (LMC) is an irregular satellite galaxy of the Milky Way, which has been observed extensively in Very-High-Energy (VHE) gamma rays with the H.E.S.S. telescopes since 2004 and reaches now a total observation time of…

X-ray extragalactic surveys are ideal laboratories for the study of the evolution and clustering of active galactic nuclei (AGN). The XXL Survey spans two fields of a combined 50 $deg^2$ observed for more than 6Ms with XMM-Newton, occupying…

Despite the growing number of gamma-ray sources detected by Fermi-LAT, about one third of the sources in each survey remains of uncertain type. We present a new deep neural network approach for the classification of unidentified or…

We show that using mid-IR color selection to find active galactic nuclei (AGN) is as effective in dense stellar fields such as the Magellanic Clouds as it is in extragalactic fields with low stellar densities using comparisons between the…

The application of multi-wavelength selection techniques is crucial for discovering a complete and unbiased set of Active Galactic Nuclei (AGNs). Here, we select a sample of 72 AGN candidates in the Extended Groth Strip (EGS) using deep…

The faint radio-source population includes sources dominated both by star formation and active galactic nuclei (AGN), encoding the evolution of activity in the Universe. To investigate its nature, we probabilistically classified 4,471 radio…

Globular clusters (GCs) have been at the heart of many longstanding questions in many sub-fields of astronomy and, as such, systematic identification of GCs in external galaxies has immense impacts. In this study, we take advantage of M87's…

Dusty stellar point sources are a significant stage in stellar evolution and contribute to the metal enrichment of galaxies. These objects can be classified using photometric and spectroscopic observations with color-magnitude diagrams…

Automatic source detection and classification tools based on machine learning (ML) algorithms are growing in popularity due to their efficiency when dealing with large amounts of data simultaneously and their ability to work in…

We used data from the QUEST-La Silla Active Galactic Nuclei (AGN) variability survey to construct light curves for 208,583 sources over $\sim 70$ deg$^2$, with a a limiting magnitude $r \sim 21$. Each light curve has at least 40 epochs and…

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…

The Advanced Satellite for Cosmology and Astrophysics (ASCA) has made multiple observations of the Small Magellanic Cloud (SMC). X-ray mosaic images in the soft (0.7--2.0 keV) and hard (2.0--7.0 keV) bands are separately constructed, and…

We carry out classification of 4330 X-ray sources in the 2XMMi-DR3 catalog. They are selected under the requirement of being a point source with multiple XMM-Newton observations and at least one detection with the signal-to-noise ratio…

The classifications of Fermi-LAT unassociated sources are studied using multiple machine learning (ML) methods. The update data from 4FGL-DR3 are divided into high Galactic latitude (HGL, Galactic latitude $|b|>10^\circ$) and low Galactic…

We present the result of a spectroscopic campaign targeting Active Galactic Nucleus (AGN) candidates selected using a novel unsupervised machine-learning (ML) algorithm trained on optical and mid-infrared (mid-IR) photometry. AGN candidates…

We report the first search for new star clusters performed using the VISTA near-infrared YJKs Magellanic Clouds survey (VMC) data sets. We chose a pilot field of ~ 0.4 deg^2 located in the South-West of the Small Magelllanic Cloud (SMC)…