5 papers
Performance of morphological classifiers for galaxy mergers compared to current machine learning methods
Aidan P. Cotter, William J. pearson, Subhrata Dey +3
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…
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…
statmorph-lsst: Quantifying and correcting morphological biases in galaxy surveys
Elizaveta Sazonova, Cameron R. Morgan, Michael Balogh +16
Quantitative morphology provides a key probe of galaxy evolution across cosmic time and environments. However, these metrics can be biased by changes in imaging quality - resolutio…
Galaxy Zoo: Cosmic Dawn -- morphological classifications for over 41,000 galaxies in the Euclid Deep Field North from the Hawaii Two-0 Cosmic Dawn survey
James Pearson, Hugh Dickinson, Stephen Serjeant +22
We present morphological classifications of over 41,000 galaxies out to across six square degrees of the Euclid Deep Field North (EDFN) from the Hawaii Twenty…
Classifying merger stages with adaptive deep learning and cosmological hydrodynamical simulations
Rosa de Graaff, Berta Margalef-Bentabol, Lingyu Wang +4
Hierarchical merging of galaxies plays an important role in galaxy formation and evolution. Mergers could trigger key evolutionary phases such as starburst activities and active ac…