Related papers: Classifying Stars, Galaxies and AGN in CLAUDS+HSC-…
We present BayeSED, a general purpose tool for doing Bayesian analysis of SEDs by using whatever pre-existing model SED libraries or their linear combinations. The artificial neural networks (ANNs), principal component analysis (PCA) and…
Modern cosmological surveys such as the Hyper Suprime-Cam (HSC) survey produce a huge volume of low-resolution images of both distant galaxies and dim stars in our own galaxy. Being able to automatically classify these images is a…
We characterize infrared spectral energy distributions of 343 (Ultra) Luminous Infrared Galaxies from $z=0.3-2.8$. We diagnose the presence of an AGN by decomposing individual Spitzer mid-IR spectroscopy into emission from star-formation…
Abstract abridged. The eROSITA X-ray telescope aboard the SRG orbital observatory, in the course of its all-sky survey, is expected to detect about three million active galactic nuclei (AGN) and hundred thousand clusters and groups of…
A total of 235 Active Galactic Nuclei (AGN) from two different soft X-ray surveys (the ROSAT Deep Survey -DRS- and the ROSAT International X-ray Optical Survey -RIXOS-) with redshifts between 0 and 3.5 are used to study the clustering of…
Variability is a property shared by practically all AGN. This makes variability selection a possible technique for identifying AGN. Given that variability selection makes no prior assumption about spectral properties, it is a powerful…
We measure the clustering of X-ray, radio, and mid-IR-selected active galactic nuclei (AGN) at 0.2 < z < 1.2 using multi-wavelength imaging and spectroscopic redshifts from the PRIMUS and DEEP2 redshift surveys, covering 7 separate fields…
The Graph Neural Network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dataset is rarely considered. Traditional methods such as…
Despite the great promise of machine-learning algorithms to classify and predict astrophysical parameters for the vast numbers of astrophysical sources and transients observed in large-scale surveys, the peculiarities of the training data…
In this work the INTEGRAL hard X-ray selected sample of AGN has been used to investigate the possible contribution of absorbing material distributed within the host galaxies to the total amount of NH measured in the X-ray band. We collected…
Active galactic nuclei (AGN) are supermassive black holes with luminous accretion disks found in some galaxies, and are thought to play an important role in galaxy evolution. However, traditional optical spectroscopy for identifying AGN…
We study the spectral classification of emission-line galaxies as star-forming galaxies or Active Galactic Nuclei (AGNs). From the Sloan Digital Sky Survey (SDSS) high quality data, we define an improved classification to be used for high…
Low luminosity active galactic nuclei (LLAGN) probe accretion physics in the low Eddington regime and can provide additional clues about galaxy evolution. AGN variability is ubiquitous and thus provides a reliable tool for finding AGN. We…
Hydrogen, the most abundant element in the universe, is crucial for understanding galaxy formation and evolution. The 21 cm neutral atomic hydrogen - HI spectral line maps the gas kinematics within galaxies, providing key insights into…
Heavily obscured Active Galactic Nuclei (AGN), especially Compton-thick sources with line-of-sight column density ($N_{\rm H,los}$) $>$ 10$^{24}$ cm$^{-2}$, are critical to understanding supermassive black hole (SMBH) growth and the origin…
In today's era, a tremendous amount of data is generated by different observatories and manual classification of data is something which is practically impossible. Hence, to classify and categorize the objects there are multiple machine and…
The Gradient Boosting Classifier (GBC) is a widely used machine learning algorithm for binary classification, which builds decision trees iteratively to minimize prediction errors. This document explains the GBC's training and prediction…
Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…
We present an application of self-adaptive supervised learning classifiers derived from the Machine Learning paradigm, to the identification of candidate Globular Clusters in deep, wide-field, single band HST images. Several methods…
The deepest all-sky survey available in the $\gamma$-ray band - the last release of the Fermi-LAT catalogue (4FGL-DR3) based on the data accumulated in 12 years, contains more than 6600 sources. The largest population among the sources is…