Related papers: Automated Classification of 2000 Bright IRAS Sourc…

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…

We describe a uniform all-sky survey of bright blazars, selected primarily by their flat radio spectra, that is designed to provide a large catalog of likely gamma-ray AGN. The defined sample has 1625 targets with radio and X-ray properties…

Modern X-ray telescopes have detected hundreds of thousands of X-ray sources in the universe. However, current methods to classify these sources using the X-ray data themselves suffer problems - detailed X-ray spectroscopy of individual…

The Fermi Large Area Telescope (Fermi-LAT) has detected more than 7,000 gamma-ray sources, a significant fraction of which are identified as blazars, while a comparable number remain classified as blazars of uncertain type (BCUs) or are…

This paper is part of large effort within the J-PAS collaboration that aims to classify point-like sources in miniJPAS, which were observed in 60 optical bands over $\sim$ 1 deg$^2$ in the AEGIS field. We developed two algorithms based on…

We present an updated atlas of spectra from the Low-Resolution Spectrometer (LRS) on the Infrared Astronomical Satellite (IRAS), which took spectra from 7.67 to 22.73 um with a spectral resolving power (lambda / Delta lambda) of 20-60. The…

Since its launch on October 2002, the INTEGRAL satellite has revolutionized our knowledge of the hard X-ray sky thanks to its unprecedented imaging capabilities and source detection positional accuracy above 20 keV. Nevertheless, many of…

We present an all-sky sample of ~ 1.4 million AGNs meeting a two color infrared photometric selection criteria for AGNs as applied to sources from the Wide-Field Infrared Survey Explorer final catalog release (AllWISE). We assess the…

The use of Artificial Neural Networks (ANNs) as a classifier of digital spectra is investigated. Using both simulated and real data, it is shown that neural networks can be trained to discriminate between the spectra of different classes of…

(Abridged) Since its launch in October 2002, the INTEGRAL satellite has revolutionized our knowledge of the hard X-ray sky thanks to its unprecedented imaging capabilities and source detection positional accuracy above 20 keV. Nevertheless,…

Classifying Active Galactic Nuclei (AGN) is a challenge, especially for BL Lac Objects (BLLs), which are identified by their weak emission line spectra. To address the problem of classification, we use data from the 4th Fermi Catalog, Data…

Blazars are a highly-variable, radio-loud subclass of active galactic nuclei (AGN). In order to better understand such objects we must be able to easily identify candidate blazars from the growing population of unidentified sources. Working…

Several ongoing and upcoming radio telescopes aim to detect either the global 21 cm signal or the 21 cm power spectrum. The extragalactic radio background, as detected by ARCADE-2 and LWA-1, suggests a strong radio background from cosmic…

We report on a new method to identify Active Galactic Nuclei (AGN) among unidentified INTEGRAL sources. This method consists of cross-correlating unidentified sources listed in the fourth IBIS Survey Catalogue first with infrared and then…

The 120,000 X-ray sources detected in the RASS II processing of the ROSAT All-Sky Survey are correlated with the 14,315 IRAS galaxies selected from the IRAS Point Source Catalogue: 372 IRAS galaxies show X-ray emission within a distance of…

With nearly two billion stars observed and their corresponding astrometric parameters evaluated in the recent Gaia mission, the number of astrometric binary candidates have risen significantly. Due to the surplus of astrometric data, the…

We select the Luminous Infrared Galaxies by cross-correlating the Faint Source Catalogue (FSC) and Point Source Catalogue (PSC) of the IRAS Survey with the Second Data Release of the SDSS for studying their infrared and optical properties.…

A large number of unidentified sources found by astronomical surveys and other observations necessitate the use of an automated classification technique based on machine learning methods. The aim of this paper is to find a suitable…

Observed Young Stellar Objects (YSOs) are used to study star formation and characterize star forming regions. For this purpose, YSO candidate catalogs are compiled from various surveys, especially in the infrared (IR), and simple selection…

Machine learning algorithms based on artificial neural networks have proven very useful for a variety of classification problems. Here we apply them to a well-known problem in crystallography, namely the classification of X-ray diffraction…