Related papers: A versatile classification tool for galactic activ…

Reliable, versatile galaxy activity diagnostics are essential for understanding galaxy evolution. Traditional methods frequently necessitate extensive preprocessing, such as starlight subtraction and emission line deblending (e.g.,…

(abridged) The overwhelming majority of diagnostic tools for galactic activity are focused on active galaxies. Passive or dormant galaxies are often excluded from these diagnostics which usually employ emission line features. In this work,…

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.…

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…

Context. A defining characteristic of active galactic nuclei (AGN) that distinguishes them from other astronomical sources is their stochastic variability, which is observable across the entire electromagnetic spectrum. Upcoming optical…

Classification of intermediate redshift ($z$ = 0.3--0.8) emission line galaxies as star-forming galaxies, composite galaxies, active galactic nuclei (AGN), or low-ionization nuclear emission regions (LINERs) using optical spectra alone was…

Star-formation activity is a key property to probe the structure formation and hence characterise the large-scale structures of the universe. This information can be deduced from the star formation rate (SFR) and the stellar mass (Mstar),…

(abridged) We develop a tool for the automated spectral classification of OB stars according to their sub-types. We use the regular Random Forest (RF) algorithm, the Probabilistic RF (PRF), and we introduce the KDE-RF method which is a…

The survey of the COSMOS field by the VLT Survey Telescope is an appealing testing ground for variability studies of active galactic nuclei (AGN). With 54 r-band visits over 3.3 yr and a single-visit depth of 24.6 r-band mag, the dataset is…

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 Fermi Gamma-ray Space Telescope is producing the most detailed inventory of the gamma-ray sky to date. Despite tremendous achievements approximately 25% of all Fermi extragalactic sources in the Second Fermi LAT Catalogue (2FGL) are…

Context. A major challenge in astrophysics is classifying galaxies by their activity. Current methods often require multiple diagnostics to capture the full range of galactic activity. Furthermore, overlapping excitation sources with…

In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…

We present a method for automated classification of galaxies with low signal-to-noise (S/N) spectra typical of redshift surveys. We develop spectral simulations based on the parameters for the 2dF Galaxy Redshift Survey, and with these…

This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…

Classifying Active Galactic Nuclei (AGN) is a challenge, especially for BL Lac Objects (BLLs), which are identified by their weak emission line spectra. To address the problem of classification, we use data from the 4th Fermi Catalog, Data…

With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series…

Classifying catalog objects as stars, galaxies, or AGN is a crucial part of any statistical study of galaxies. We describe our pipeline for binary (star/galaxy) and multiclass (star/galaxy/Type I AGN/Type II AGN) classification developed…

Random Forest (RF) is a widely used ensemble learning technique known for its robust classification performance across diverse domains. However, it often relies on hundreds of trees and all input features, leading to high inference cost and…

In this paper we discuss an application of machine learning based methods to the identification of candidate AGN from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine…