Related papers: Efficient Fermi Source Identification with Machine…
The identification of low-energy counterparts for gamma-ray sources is one of the biggest challenge in modern gamma-ray astronomy. Recently, we developed and successfully applied a new association method to recognize gamma-ray blazar…
Context. Active galactic nuclei (AGNs) and star forming galaxies (SFGs) are the primary sources of extragalactic radio sky. But it is difficult to distinguish the radio emission produced by AGNs from that by SFGs, especially when the radio…
We present a machine learning model to classify Active Galactic Nuclei (AGN) and galaxies (AGN-galaxy classifier) and a model to identify type 1 (optically unabsorbed) and type 2 (optically absorbed) AGN (type 1/2 classifier). We test…
Revealing the nature of unassociated high-energy (> 100 MeV) gamma-ray sources remains a challenge 35 years after their discovery. Of the 934 gamma-ray sources at high Galactic latitude (|b| > 15 degrees) in the First Fermi-LAT catalogue…
Extensive astronomical surveys, like those conducted with the {\em Chandra} X-ray Observatory, detect hundreds of thousands of unidentified cosmic sources. Machine learning (ML) methods offer an efficient, probabilistic approach to classify…
Searching for low energy counterparts of gamma-ray sources is one of the major challenges in modern gamma-ray astronomy. In the third Fermi source catalog about 30 % of detected sources are unidentified/unassociated Gamma-ray Sources…
We present the first Fermi Large Area Telescope (LAT) low energy catalog (1FLE) of sources detected in the energy range 30 - 100 MeV. The COMPTEL telescope detected sources below 30 MeV, while catalogs released by the Fermi-LAT and EGRET…
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…
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…
In this paper we discuss an application of machine learning based methods to the identification of candidate AGN from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine…
We have analyzed all the archival X-ray data of 134 unidentified (unID) gammaray sources listed in the first Fermi/LAT (1FGL) catalog and subsequently followed up by Swift/XRT. We constructed the spectral energy distributions (SEDs) from…
Context. Based on their overwhelming dominance among associated Fermi gamma ray catalogue sources, it is expected that a large fraction of the unidentified Fermi objects are blazars. Through crossmatching between the positions of…
We collect data at all frequencies for the new sources classified as unknown active galactic nuclei (AGNs) in the latest Burst Alert Telescope (BAT) all-sky hard X-ray catalog. Focusing on the 36 sources with measured redshift, we compute…
A sample of 312 low-frequency peaked BL Lacertae objects (LBLs) and 694 flat spectrum radio quasars (FSRQs) with the parameters both redshift and $\gamma$-ray photon spectral index ($\Gamma _\gamma$) is compiled from the active galactic…
We present a morphological classification of 14,245 radio active galactic nuclei (AGNs) into six types, i.e., typical Fanaroff--Riley Class I / II (FRI/II), FRI/II-like bent-tailed, X-shaped radio galaxy, and ringlike radio galaxy, by…
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…
Dark matter annihilation signals coming from Galactic subhaloes may account for a small fraction of unassociated point sources detected in the Second Fermi-LAT catalogue (2FGL). To investigate this possibility, we present Sibyl, a Random…
The Large Area Telescope (LAT) aboard the Fermi satellite allows us to study the high-energy gamma-ray sky with unprecedented sensitivity. However, the origin of 31% of the detected gamma-ray sources remains unknown. This population of…
The optical spectroscopic followup of 27 sources belonging to a sample of 30 high-energy objects selected by positionally cross correlating the first Fermi/LAT Catalog and the ROSAT All-Sky Survey Bright Source Catalog is presented here. It…
We investigate the physical nature of active galactic nuclei (AGNs) using machine learning (ML) tools. We show that the redshift, $z$, bolometric luminosity, $L_{\rm Bol}$, central mass of the supermassive black hole (SMBH), $M_{\rm BH}$,…