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Leveraging Transfer Learning for Astronomical  Object Classification: A Study of Convolutional  Neural Network Architectures
  • Younes Ghazagh Jahed,
  • Kamand Sarvari
Younes Ghazagh Jahed

Corresponding Author:[email protected]

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Kamand Sarvari


Astronomical object classification plays a crucial role in understanding the universe and its phenomena. In this study, the performance of pretrained convolutional neural network (CNN) architectures in classifying astronomical objects is evaluated using datasets from the Sloan Digital Sky Survey (SDSS). Five models—VGG19, ResNet101V2, ResNet152V2, DenseNet169, and DenseNet201—are analyzed for their accuracy in categorizing galaxies, stars, and quasars. The findings reveal ResNet152V2 as the top performer, achieving an accuracy of 92.09% and an AUC value of 0.989. These results underscore the significance of selecting appropriate pretrained models for accurate astronomical object classification.
02 Mar 2024Submitted to TechRxiv
04 Mar 2024Published in TechRxiv