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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.,…

We use the Random Forest (RF) algorithm to develop a tool for automated activity classification of galaxies into 5 different classes: Star-forming (SF), AGN, LINER, Composite, and Passive. We train the algorithm on a combination of mid-IR…

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.…

(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,…

While it is well known that galaxies are composites of many emission processes, quantifying the various contributions remains challenging. In this work, we use unsupervised machine learning based clustering algorithms to evaluate the…

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 introduce a novel galaxy classification methodology based on the visible spectra of a sample of over 68,000 nearby ($z\leq 0.1$) Sloan Digital Sky Survey lenticular (S0) galaxies. Unlike traditional diagnostic diagrams, which rely on a…

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…

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…

We present some of the first science data with the new Keck/MOSFIRE instrument to test the effectiveness of different AGN/SF diagnostics at z~1.5. MOSFIRE spectra were obtained in three H-band multi-slit masks in the GOODS-S field,…

Two-dimensional (2D) line ratio diagnostic diagrams have become a key tool in understanding the excitation mechanisms of galaxies. The curves used to separate the different regions - HII-like or else excited by an active galactic nucleus…

The triggering mechanisms for Active Galactic Nuclei (AGN) are still debated. Some of the most popular ones include galaxy interactions (IT) and disk instabilities (DI). Using an advanced semi analytic model (SAM) of galaxy formation,…

We assemble a sample of 733 dwarf galaxies ($M_{\ast} \le 10^{9.5} \text{M}_\odot$) with signatures of active galactic nuclei (AGN) and explore the intersection between different AGN selection techniques. Objects in our database are…

AGN feedback is believed to play an important role in shaping a variety of observed galaxy properties, as well as the evolution of their stellar masses and star formation rates. In particular, in the current theoretical paradigm of galaxy…

Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for…

We introduce the Mass-Excitation (MEx) diagnostic to identify active galactic nuclei (AGN) in galaxies at intermediate redshift. In the absence of near-infrared spectroscopy, necessary to use traditional nebular line diagrams at z>0.4, we…

In a series of papers based on the FIRST and Sloan Digital Sky Surveys (SDSS), we investigate the local population of star-forming galaxies and Active Galactic Nuclei (AGN) in order to clarify the link between these two types of activity,…

To fully take advantage of the data provided by large-scale structure surveys, we need to quantify the potential impact of baryonic effects, such as feedback from active galactic nuclei (AGN) and star formation, on cosmological observables.…

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…

In the fourth \emph{Fermi} Large Area Telescope source catalog (4FGL), 5064 $\gamma$-ray sources are reported, including 3207 active galactic nuclei (AGNs), 239 pulsars, 1336 unassociated sources, 92 sources with weak association with…