Morpho-photometric Classification of KiDS DR5 Sources Based on Neural Networks: A Comprehensive Star–Quasar–Galaxy Catalog

Abstract We present a novel multimodal neural network (MNN) for classifying astronomical sources in multiband ground-based observations, from optical to near-infrared (NIR), to separate sources in stars, galaxies, and quasars. Our approach combines a convolutional neural network branch for learning morphological features from r -band images with an artificial neural network branch for extracting spectral energy distribution (SED) information. Specifically, we have used nine-band optical ( ugri ) and NIR ( ZYHJK s ) data from the Kilo-Degree Survey (KiDS) Data Release 5. The two branches of the network are concatenated and feed into fully connected layers for final classification. We train the network on a spectroscopically confirmed sample from the Sloan Digital Sky Survey crossmatched with KiDS. 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 ≤ 23 mag 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.