Convolutional Neural Networks for Classifying Galaxy Mergers: Can Faint Tidal Features Aid in Classifying Mergers?

Identifying mergers from observational data has long been a crucial aspect of studying galaxy evolution and formation. Tidal features, typically fainter than 26 magarcsec−2 , exhibit a diverse range of appearances depending on merger characteristics and are expected to be investigated in greater detail with the Rubin Observatory Legacy Survey of Space and Time (LSST), which will reveal the low-surface-brightness Universe with unprecedented precision. Our goal is to assess the feasibility of developing a convolutional neural network (CNN) that can distinguish between mergers and nonmergers based on LSST-like deep images. To this end, we used Illustris TNG50, one of the highest-resolution cosmological hydrodynamic simulations to date, allowing us to generate LSST-like mock images with a depth ∼29 magarcsec−2 for low-redshift (z = 0.16) galaxies, with labeling based on their merger status as ground truth. We focused on 151 Milky Way–like galaxies in field environments, comprising 81 nonmergers and 70 mergers. After applying data augmentation and hyperparameter tuning, a CNN model was developed with an accuracy of 65%–67%. Through additional image processing, the model was further optimized, achieving an accuracy of 67%–70% when trained on images containing only faint features. This represents an improvement of ∼5% compared to training on images with bright features only. This suggests that faint tidal features can serve as effective indicators for distinguishing between mergers and nonmergers. Future directions for improvement motivated by this study are also discussed.