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astro-ph.GA2026
Performance of morphological classifiers for galaxy mergers compared to current machine learning methods
Aidan P. Cotter, William J. Pearson, William J. pearson +4
Aims. Non-parametric morphological statistics can be used for efficient classification of galaxy mergers. This work aims to compare the performance of morphological merger classifi…
astro-ph.GA2026
The TNG50-SKIRT Atlas: Multi-wavelength nonparametric galaxy morphology
Sena Bokona Tulu, Maarten Baes, Angelos Nersesian +5
Context: Galaxy morphology is a fundamental property to describe galaxy evolution. However, the observed morphology of a particular galaxy may depend on the observed wavelength. Ai…