Related papers: A versatile classification tool for galactic activ…

We use $\sim 1,000$ X-ray sources in the COSMOS-Legacy survey and study the position of the AGN relative to the star forming main sequence (MS). We also construct a galaxy (non-AGN) reference sample that includes $\sim 90,000$ sources. We…

We have investigated and applied machine-learning algorithms for Infrared Colour Selection of Galactic Wolf-Rayet (WR) candidates. Objects taken from the GLIMPSE catalogue of the infrared objects in the Galactic plane can be classified into…

We present a catalog of spectro-photometric redshifts for 1308 galaxies from the GRism ACS Program for Extragalactic Science (GRAPES) observations with the Hubble Space Telescope. These low-resolution spectra between 6000 A and 9500 A are…

We present a star/galaxy classification for the Southern Photometric Local Universe Survey (S-PLUS), based on a Machine Learning approach: the Random Forest algorithm. We train the algorithm using the S-PLUS optical photometry up to $r$=21,…

In this work, we investigate the dependence of the covering factor (CF) of active galactic nuclei (AGNs) (i) on the mid-infrared (MIR) luminosity and (ii) on the redshift. We constructed 12- and 22-micron luminosity functions (LFs) at 0.006…

Quantifying galaxy morphology is a challenging yet scientifically rewarding task. As the scale of data continues to increase with upcoming surveys, traditional classification methods will struggle to handle the load. We present a solution…

We introduce redMaGiC, an automated algorithm for selecting Luminous Red Galaxies (LRGs). The algorithm was specifically developed to minimize photometric redshift uncertainties in photometric large-scale structure studies. redMaGiC…

Automating real-time anomaly detection is essential for identifying rare transients, with modern survey telescopes generating tens of thousands of alerts per night, and future telescopes, such as the Vera C. Rubin Observatory, projected to…

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…

We propose a random forest (RF) machine learning approach to determine the accreted stellar mass fractions ($f_\mathrm{acc}$) of central galaxies, based on various dark matter halo and galaxy features. The RF is trained and tested using…

We apply a number of statistical and machine learning techniques to classify and rank gamma-ray sources from the Third Fermi Large Area Telescope (LAT) Source Catalog (3FGL), according to their likelihood of falling into the two major…

Measuring the redshift of active galactic nuclei (AGNs) requires the use of time-consuming and expensive spectroscopic analysis. However, obtaining redshift measurements of AGNs is crucial as it can enable AGN population studies, provide…

We use automated surface photometry and pattern classification techniques to morphologically classify galaxies. The two-dimensional light distribution of a galaxy is reconstructed using Fourier series fits to azimuthal profiles computed in…

The rapid increase in data on galaxy images at low and high redshift calls for re-examination of the classification schemes and for new automatic objective methods. Here we present a classification method by Artificial Neural Networks. We…

We have studied the star-formation and AGN activity of massive galaxies in the redshift range $z=0.4-2$, which are detected in a deep survey field using the AKARI InfraRed (IR) astronomical satellite and {\em Subaru} telescope toward the…

Reliably identifying active galactic nuclei (AGNs) in dwarf galaxies is key to understanding black hole demographics at low masses and constraining models for black hole seed formation. Here we present Chandra X-ray Observatory observations…

We present a spectroscopic study with the derivation of the physical properties of 37 Balmer break galaxies, which have the necessary lines to locate them in star-forming-AGN diagnostic diagrams. These galaxies span a redshift range from…

Understanding galaxy classification depends on our interpretation of their spectra. To date, the hydrogen Balmer lines remain the most consistent way to classify galaxies, but at 'intermediate' redshifts ($1.5 < z < 2.5$), galaxies are hard…

We study a combined sample of 264 star-forming, 51 composite, and 73 active galaxies using optical spectra from SDSS and mid-infrared (mid-IR) spectra from the Spitzer Infrared Spectrograph. We examine optical and mid-IR spectroscopic…

Using artificial neural network (ANN) predictions of total infra-red luminosities (LIR), we compare the host galaxy star formation rates (SFRs) of ~21,000 optically selected active galactic nuclei (AGN), 466 low excitation radio galaxies…