Beyond the Brightest: A Deep Learning Approach to Identifying Major and Minor Galaxy Mergers in CANDELS at z ~ 1

Galaxy mergers play an important role in many aspects of galaxy evolution, therefore, more accurate merger identifications are paramount for achieving a complete understanding of galaxy evolution. As we enter the era of very large imaging surveys, we are able to observe mergers extending to even lower masses and higher redshifts. Despite low-mass galaxies being more common, many previous merger identification methods were mostly calibrated for high-mass, local galaxies, which are easier to identify. To prepare for upcoming surveys, we train a convolutional neural network (CNN) using mock HST CANDELS images at z ~ 1 created from the IllustrisTNG50 cosmological simulation. We successfully identify galaxy mergers between a wide range of galaxies (10⁸M_ < M_ < 1012.5M_, and $μ>1:10$), achieving overall accuracy, purity, and completeness of ~73%. We show, for the first time, that a CNN trained on this diverse set of galaxies is capable of identifying both major and minor mergers, early and late stage mergers, as well as nonmerging galaxies, similar to that of networks trained at lower redshifts and/or higher masses (with accuracies ranging between $66-80%$) for the first time. We discuss the inherent limits of galaxy merger identification due to orientation angle and explore the confounding variables, such as star formation, to consider when applying to real data. This network enables the exploration of the impact of previously overlooked mergers of high mass ratio and low stellar masses on galaxy evolution in CANDELS, and can be expanded to surveys from JWST, Rubin, Roman, and Euclid.