4 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…
Super-resolving Herschel - a deep learning based deconvolution and denoising technique
Dennis Koopmans, Lingyu Wang, Berta Margalef-Bentabol +9
Dusty star-forming galaxies (DSFGs) dominate the far-infrared and sub-millimetre number counts, but single-dish surveys suffer from poor angular resolution, complicating mult-wavel…
AGN -- host galaxy photometric decomposition using a fast, accurate and precise deep learning approach
Berta Margalef-Bentabol, Lingyu Wang, Antonio La Marca +1
Identifying active galactic nuclei (AGN) is extremely important for understanding galaxy evolution and its connection with the assembly of supermassive black holes (SMBH). With the…
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…