Related papers: How to Find Variable Active Galactic Nuclei with M…
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…
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…
We conduct an analysis of over 60,000 dwarf galaxies (7<=log(M_*/M_\odot)<=10) in search of photometric variability indicative of active galactic nuclei (AGNs). Using data from the Young Supernova Experiment (YSE), a time domain survey on…
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…
We present an analysis of the nuclear variability of $\sim28,000$ nearby ($z<0.15$) galaxies with Sloan Digital Sky Survey (SDSS) spectroscopy in Stripe 82. We construct light curves using difference imaging of SDSS g-band images, which…
In deep X-ray surveys, active galactic nuclei (AGNs) with a broad range of luminosities have been identified. However, cosmologically distant low-luminosity AGN (LLAGN, $L_{\mathrm{X}} \lesssim 10^{42}$ erg s$^{-1}$) identification still…
Variability is a main property of active galactic nuclei (AGN) and it was adopted as a selection criterion using multi epoch surveys conducted for the detection of supernovae (SNe). We have used two SN datasets. First we selected the AXAF…
We propose a new method for identifying active galactic nuclei (AGN) in low mass ($\rm M_*\leq10^{10}M_\odot$) galaxies. This method relies on spectral energy distribution (SED) fitting to identify galaxies whose radio flux density has an…
We used data from the QUEST-La Silla Active Galactic Nuclei (AGN) variability survey to construct light curves for 208,583 sources over $\sim 70$ deg$^2$, with a a limiting magnitude $r \sim 21$. Each light curve has at least 40 epochs and…
Optical variability has proven to be an effective way of detecting AGNs in imaging surveys, lasting from weeks to years. In the present work we test its use as a tool to identify AGNs in the VST multi-epoch survey of the COSMOS field,…
We aim to study the effect of environment on the presence and fuelling of Active Galactic Nuclei (AGN) in massive galaxy clusters. We explore the use of different AGN detection techniques with the goal of selecting AGN across a broad range…
Distinguishing active galaxies from star-forming galaxies is essential for understanding galaxy evolution. Diagnostic methods like the BPT (Baldwin, Phillips, and Terlevich) diagram use optical emission-line ratios to separate galaxies.…
Determining the frequency and duration of changing--look (CL) active galactic nuclei (AGNs) phenomena, where the optical broad emission lines appear or disappear, is crucial to understand the evolution of the accretion flow around…
In a previous paper, we studied two statistical methods used to analyse the variability of active galactic nuclei (AGNs): the C and F statistics. Applying them to observed differential light-curves of 39 AGNs, we found that, even though the…
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…
Active Galactic Nuclei (AGN) sources feature supermassive black holes that launch relativistic plasma jets. They are key $\gamma$-ray sources providing a unique laboratory for studying extreme particle acceleration and plasma physics.…
Classifying variable stars is crucial for advancing our understanding of stellar evolution and dynamics. As large-scale surveys generate increasing volumes of light curve data, the demand for automated and reliable classification techniques…
We present the multi-wavelength and environmental properties of 37 variability-selected active galactic nuclei (AGNs), including 30 low luminosity AGNs (LLAGNs), using a high cadence time-domain survey (ASAS-SN) from a spectroscopic sample…
A novel application of machine-learning (ML) based image processing algorithms is proposed to analyze an all-sky map (ASM) obtained using the Fermi Gamma-ray Space Telescope. An attempt was made to simulate a one-year ASM from a…
We assess the systematics and efficiency of an AGN selection method based on mid-infrared (MIR) variability. To this end, we utilize various types of active and inactive galaxies from the Sloan Digital Sky Survey, matching them with…