Related papers: AGN selection in the AKARI NEP deep field with the…

The aim of this work is to develop a comprehensive method for classifying sources in large sky surveys and we apply the techniques to the VIMOS Public Extragalactic Redshift Survey (VIPERS). Using the optical (u*, g', r', i') and NIR data…

The recently published fourth Fermi Large Area Telescope source catalog (4FGL) reports 5065 gamma-ray sources in terms of direct observational gamma-ray properties. Among the sources, the largest population is the Active Galactic Nuclei…

Machine-learning (ML) algorithms will play a crucial role in studying the large datasets delivered by new facilities over the next decade and beyond. Here, we investigate the capabilities and limits of such methods in finding galaxies with…

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…

Data mining is an important and challenging problem for the efficient analysis of large astronomical databases and will become even more important with the development of the Global Virtual Observatory. In this study, learning vector…

Variability is a property shared by virtually all active galactic nuclei (AGNs), and was adopted as a criterion for their selection using data from multi epoch surveys. Low Luminosity AGNs (LLAGNs) are contaminated by the light of their…

In order to understand the interaction between the central black hole and the whole galaxy or their co-evolution history along with cosmic time, a complete census of active galactic nuclei (AGN) is crucial. However, AGNs are often missed in…

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…

Variability has proven to be a powerful tool to detect active galactic nuclei (AGN) in multi-epoch surveys. The new-generation facilities expected to become operational in the next few years will mark a new era in time-domain astronomy and…

We use a combination of the XMM-Newton serendipitous X-ray survey with the optical SDSS, and the infrared WISE all-sky survey in order to check the efficiency of the low X-ray to infrared luminosity selection method in finding heavily…

We searched the Northern Hemisphere Fields of the GALEX Time-Domain Survey (TDS) for galaxies with UV variability indicative of active galactic nuclei (AGNs). We identified 48 high-probability candidate AGNs from a parent sample of 1819…

We report a new sample of obscured active galactic nuclei (AGNs) selected from the XMM serendipitous source and AKARI point-source catalogs. We match X-ray sources with infrared (18 and 90 micron) sources located at |b|>10 deg to create a…

Identifying AGNs in dwarf galaxies is critical for understanding black hole formation but remains challenging due to their low luminosities, low metallicities, and star formation-driven emission that can obscure AGN signatures. Machine…

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 propose a novel weighted combination feature selection method using bootstrap and fuzzy sets. The proposed method mainly consists of three processes, including fuzzy sets generation using bootstrap, weighted combination of…

X-rays provide a robust method in identifying AGN. However, in the high-redshift Universe, their space density is relatively low, and, in combination with the small areas covered by X-ray surveys, the selected AGN are poorly sampled. Deep…

The nature of an atom in a bonded structure -- such as in molecules, in nanoparticles or solids, at surfaces or interfaces -- depends on its local atomic environment. In atomic-scale modeling and simulation, identifying groups of atoms with…

We present results of the 2.5-5 {\mu}m spectroscopy of a sample of hard X-ray selected active galactic nuclei (AGNs) using the grism mode of the InfraRed Camera (IRC) on board the infrared astronomical satellite AKARI. The sample is…

Deep neural networks (DNNs) demonstrate great success in classification tasks. However, they act as black boxes and we don't know how they make decisions in a particular classification task. To this end, we propose to distill the knowledge…

Fuzzy Neural Networks (FNNs) are effective machine learning models for classification tasks, commonly based on the Takagi-Sugeno-Kang (TSK) fuzzy system. However, when faced with high-dimensional data, especially with noise, FNNs encounter…