Deep Learning Applications Based on WISE Infrared Data: Classification of Stars, Galaxies and Quasars
This provides a more accurate classification method for astronomers dealing with large-scale WISE infrared survey data, though it is incremental as it builds on existing deep learning techniques.
The paper tackled the challenge of classifying stars, galaxies, and quasars from WISE infrared data using a deep learning network called IICnet, achieving high accuracies of 96.2% for galaxies, 97.9% for quasars, and 96.4% for stars, with AUC over 99%.
The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. However, classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses. In this paper, the deep learning classification network, IICnet (Infrared Image Classification network), is designed to classify sources from WISE images to achieve a more accurate classification goal. IICnet shows good ability on the feature extraction of the WISE sources. Experiments demonstrates that the classification results of IICnet are superior to some other methods; it has obtained 96.2% accuracy for galaxies, 97.9% accuracy for quasars, and 96.4% accuracy for stars, and the Area Under Curve (AUC) of the IICnet classifier can reach more than 99%. In addition, the superiority of IICnet in processing infrared images has been demonstrated in the comparisons with VGG16, GoogleNet, ResNet34, MobileNet, EfficientNetV2, and RepVGG-fewer parameters and faster inference. The above proves that IICnet is an effective method to classify infrared sources.