Analysing the prevalence of tidal features in HSC-SSP using self-supervised representation learning

Abstract

We use a combination of self-supervised machine learning and visual classification to identify tidal features in a sample of 34 331 galaxies with stellar masses and redshift, drawn from the Hyper Suprime-Cam Subaru Strategic Programme optical imaging survey. We assemble the largest sample of 1646 galaxies with confirmed tidal features, finding a tidal feature fraction. We analyse how the incidences of tidal features and the various classes of tidal features vary with host galaxy stellar mass, photometric redshift, and colour, as well as halo mass. We find an increasing relationship between tidal feature fraction and host galaxy stellar mass, and a decreasing relationship with redshift. We find more tidal features occurring in group environments with