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We present a study of the optical spectral properties of 115 ultraluminous infrared galaxies (ULIRGs) in the southern sky. Using the optical spectra obtained at CTIO 4 m and provided by the 2dF Galaxy Redshift Survey and the 6dF Galaxy…

The origin of huge infrared luminosities of ultraluminous infrared galaxies (ULIGs) is still in question. Recently, Genzel et al. made mid-infrared (MIR) spectroscopy of a large number of ULIGs and found that the major energy source in them…

We present an enhanced unsupervised machine learning (UML) module within our previous \texttt{USmorph} classification framework featuring two components: (1) hierarchical feature extraction via a pre-trained ConvNeXt convolutional neural…

Optical emission-line diagnostic diagrams, such as the classical BPT, are widely used to identify ionisation mechanisms in galaxies but often suffer from degeneracies, especially when multiple sources coexist. We critically evaluate the…

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.…

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…

Low-ionization nuclear emission-line regions (LINERs), which exist in a large fraction of galaxies, may be the least luminous manifestation of quasar activity. The nature of LINERs has, however, remained controversial because an AGN-like…

Anomalies are samples that significantly deviate from the rest of the data and their detection plays a major role in building machine learning models that can be reliably used in applications such as data-driven design and novelty…

The integration of Artificial Intelligence (AI) in medical diagnostics is often hindered by model opacity, where high-accuracy systems function as "black boxes" without transparent reasoning. This limitation is critical in clinical…

Automated methods for classifying extragalactic objects in large surveys offer significant advantages compared to manual approaches in terms of efficiency and consistency. However, the existence of the Galactic disk raises additional…

The classification of the optical spectra of active galactic nuclei (AGN) into different types is well founded on AGN physics, but it involves some degree of human oversight and cannot be reliably scaled to large data sets. Machine learning…

We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…

Using the large emission line galaxy sample from the Sloan Digital Sky Survey we show that Star forming galaxies, Seyferts, and low-ionization nuclear emission-line regions (LINERs) form clearly separated branches on the standard optical…

Uniform Manifold Approximation and Projection (UMAP) is a widely used manifold learning technique for dimensionality reduction. This paper studies UMAP, supervised UMAP, and several competing dimensionality reduction methods, including…

We have searched for anomalous events using 2,520 hours of archival observations from Murriyang, CSIRO's Parkes radio telescope. These observations were originally undertaken to search for pulsars. We used a machine-learning algorithm based…

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…

Dwarf AGN serve as the ideal systems for identifying intermediate mass black holes (IMBHs) down to the most elusive regimes ($\sim 10^3 - 10^4 M_{\odot}$). However, the ubiquitously metal-poor nature of dwarf galaxies gives rise to…

Traditional single-fibre spectroscopy provides a single galaxy spectrum, forming the basis for crucial parameter estimation. However, its accuracy can be compromised by various sources of contamination, such as the prominent \Ha~emission…

We present the results of Spitzer IRS low-resolution infrared 5-35 micron spectroscopy of nearby ultraluminous infrared galaxies (ULIRGs) at z < 0.15. We focus on the search for the signatures of buried active galactic nuclei (AGNs) in the…

Machine learning has emerged as a powerful tool in the field of gamma-ray astrophysics. The algorithms can distinguish between different source types, such as blazars and pulsars, and help uncover new insights into the high-energy universe.…