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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
From DES to KiDS: Domain adaptation for cross-survey detection of low-surface-brightness galaxies
Hareesh Thuruthipilly, Krzysztof Lisiecki, Junais +19
Low-surface-brightness galaxies (LSBGs) are vital for understanding galaxy formation, but their diffuse nature makes them challenging to detect. Upcoming large-scale surveys are ex…