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

Beyond the Brightest: A Deep Learning Approach to Identifying Major and Minor Galaxy Mergers in CANDELS at $z sim 1$
Aimee L. Schechter, Aleksandra ‘Ciprijanovi’c, Xuejian Shen, Rebecca Nevin, Julia M. Comerford, Aaron Stemo, Laura Blecha, Austin Fraley
arXiv:2510.12173v2 Announce Type: replace
Abstract: Galaxy mergers play an important role in galaxy evolution. Therefore, accurate merger identifications are paramount for achieving a complete understanding of how galaxies evolve. As we enter the era of large, deep, high-resolution imaging surveys, we can observe mergers extending to even lower masses and higher redshifts. Despite low-mass galaxies being more common, many previous merger identification methods were calibrated for high-mass galaxies, which are easier to identify. To prepare for upcoming surveys, we train a convolutional neural network (CNN) using mock $textit{HST}$ CANDELS images at $zsim1$ created from the IllustrisTNG50 cosmological simulation. We successfully identify galaxy mergers between a wide range of galaxies ($10^8M_odot arXiv:2510.12173v2 Announce Type: replace
Abstract: Galaxy mergers play an important role in galaxy evolution. Therefore, accurate merger identifications are paramount for achieving a complete understanding of how galaxies evolve. As we enter the era of large, deep, high-resolution imaging surveys, we can observe mergers extending to even lower masses and higher redshifts. Despite low-mass galaxies being more common, many previous merger identification methods were calibrated for high-mass galaxies, which are easier to identify. To prepare for upcoming surveys, we train a convolutional neural network (CNN) using mock $textit{HST}$ CANDELS images at $zsim1$ created from the IllustrisTNG50 cosmological simulation. We successfully identify galaxy mergers between a wide range of galaxies ($10^8M_odot