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

Most dynamic ensemble selection (DES) methods utilize the K-Nearest Neighbors (KNN) algorithm to estimate the competence of classifiers in a small region surrounding the query sample. However, KNN is very sensitive to the local distribution…

The north ecliptic pole (NEP) field is a natural deep field location for many satellite observations. It has been targeted manytimes since it was surveyed by the AKARI space telescope with its unique wavelength coverage from the near- to…

In order to find a fast and reliable method for selecting metal poor galaxies (MPGs), especially in large surveys and huge database, an Artificial Neural Network (ANN) method is applied to a sample of star-forming galaxies from the Sloan…

Discovering pulsars is a significant and meaningful research topic in the field of radio astronomy. With the advent of astronomical instruments such as he Five-hundred-meter Aperture Spherical Telescope (FAST) in China, data volumes and…

Redshift estimation and the classification of gamma-ray AGNs represent crucial challenges in the field of gamma-ray astronomy. Recent efforts have been made to tackle these problems using traditional machine learning methods. However, the…

Identifying Active Galactic Nuclei (AGNs) through their X-ray emission is efficient, but necessarily biased against X-ray-faint objects. We aim to characterize this bias by comparing X-ray-selected AGNs to the ones identified through…

We compare the relative merits of AGN selection at X-ray and mid-infrared wavelengths using data from moderately deep fields observed by both Chandra and Spitzer. The X-ray-selected AGN sample and associated optical follow-up are drawn from…

Obtaining a census of active galactic nuclei (AGN) activity across cosmic time is critical to our understanding of galaxy evolution and formation. Many AGN classification techniques are compromised by dust obscuration. However, very long…

We use machine learning techniques to investigate their performance in classifying active galactic nuclei (AGNs), including X-ray selected AGNs (XAGNs), infrared selected AGNs (IRAGNs), and radio selected AGNs (RAGNs). Using known physical…

We present data products from the 300 ks Chandra survey in the AKARI North Ecliptic Pole (NEP) deep field. This field has a unique set of 9-band infrared photometry covering 2-24 micron from the AKARI Infrared Camera, including mid-infrared…

Spitzer IRAC selection is a powerful tool for identifying luminous AGN. For deep IRAC data, however, the AGN selection wedges currently in use are heavily contaminated by star-forming galaxies, especially at high redshift. Using the large…

Three-way decision (3WD) is a powerful tool for granular computing to deal with uncertain data, commonly used in information systems, decision-making, and medical care. Three-way decision gets much research in traditional rough set models.…

The detection of X-ray emission constitutes a reliable and efficient tool for the selection of Active Galactic Nuclei (AGNs), although it may be biased against the most heavily absorbed AGNs. Simple mid-IR broad-band selection criteria…

Context. A series of studies have demonstrated that the Gaia multipeak method (GMP) is a very efficient technique to select active galactic nucleus (AGN) pair candidates. The number of candidates is determined by the size of the input AGN…

Feature selection is one of the most important problems in hyperspectral images classification. It consists to choose the most informative bands from the entire set of input datasets and discard the noisy, redundant and irrelevant ones. In…

The AKARI Deep Field South (ADF-S) is a large extragalactic survey field that is covered by multiple instruments, from optical to far-IR and radio. I summarise recent results in this and related fields prompted by the release of the…

To understand the cosmic accretion history of supermassive black holes, separating the radiation from active galactic nuclei (AGNs) and star-forming galaxies (SFGs) is critical. However, a reliable solution on photometrically recognising…

As recommender systems become increasingly complex, transparency is essential to increase user trust, accountability, and regulatory compliance. Neuro-symbolic approaches that integrate symbolic reasoning with sub-symbolic learning offer a…

We present the first attempt to select AGN using long-term NIR variability. By analysing the K-band light curves of all the galaxies in the UKIDSS Ultra Deep Survey, the deepest NIR survey over ~1 sq degree, we have isolated 393 variable…

We utilized the Edelson and Malkan (2012) and Stern et al. (2012) selection techniques and other methods to identify AGN candidates that were monitored during the Kepler prime and K2 missions. Subsequent to those observations, we obtained…