Uncovering Tidal Treasures: Automated Classification of faint tidal features in DECaLS Data

Tidal features are a key observable prediction of the hierarchical model of galaxy formation and contain a wealth of information about the properties and history of a galaxy. Modern wide-field surveys such as LSST and Euclid will revolutionise the study of tidal features. However, the volume of data will prohibit visual inspection to identify features, thereby motivating a need to develop automated detection methods. This paper presents a visual classification of ∼2, 000 galaxies from the DECaLS survey into different tidal feature categories: arms, streams, shells, and diffuse. We trained a Convolutional Neural Network (CNN) to reproduce the assigned visual classifications using these labels. Evaluated on a testing set where galaxies with tidal features were outnumbered ∼1 : 10, our network performed very well and retrieved a median 98.7 ± 0.3, 99.1 ± 0.5, 97.0 ± 0.8, and 99.4+0.2-0.6 per cent of the actual instances of arm, stream, shell, and diffuse features respectively for just 20percnt contamination. A modified version that identified galaxies with any feature against those without achieved scores of 0.981+0.001-0.003, 0.834+0.014-0.026, 0.974+0.008-0.004, and 0.900+0.073-0.015 for the accuracy, precision, recall, and F1 metrics, respectively. We used a Gradient-weighted Class Activation Mapping analysis to highlight important regions on images for a given classification to verify the network was classifying the galaxies correctly. This is the first demonstration of using CNNs to classify tidal features into sub-categories, and it will pave the way for the identification of different categories of tidal features in the vast samples of galaxies that forthcoming wide-field surveys will deliver.