Aims: Various galaxy merger detection methods have been applied to diverse datasets. However, it is difficult to understand how they compare. Our aim is to benchmark the relative performance of merger detection methods based on machine learning (ML). Methods: We explore six leading ML methods using three main datasets. The first dataset consists of

Aims: In this work, we carry out a comprehensive study using galaxy data from the Lockman Spectroscopic Redshift Survey using Osiris (Lockman-SpReSO) project, a far-infrared (FIR) selected sample of galaxies observed using optical spectroscopy. We analyse a sub-sample of star-forming galaxies (SFGs) with secure spectroscopic redshifts, mostly in

We study 51 jellyfish galaxy candidates in the Fornax, Antlia, and Hydra clusters. These candidates are identified using the JClass scheme based on the visual classification of wide-field, twelve-band optical images obtained from the Southern Photometric Local Universe Survey. A comprehensive astrophysical analysis of the jellyfish (JClass > 0)