Morpho-phot. classification of KiDS DR5 sources

The trained model achieves 98.76% overall accuracy on an independent testing data set, with F1-scores exceeding 95% for each class. Raising the output probability threshold, we obtain higher purity at the cost of lower completeness. We have also validated the network using external catalogs crossmatched with KiDS, correctly classifying 99.74% of a pure star sample selected from Gaia parallaxes and proper motions, and 99.74% of an external galaxy sample from the Galaxy and Mass Assembly survey, adjusted for low-redshift contamination.

We apply the trained network to 27,335,836 KiDS DR5 sources with r<=23mag to generate a new classification catalog. This MNN successfully leverages both morphological and SED information to enable efficient and robust classification of stars, quasars, and galaxies in large photometric surveys.