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\begin{document}

\title{Leveraging Transfer Learning for Astronomical~ Object Classification: A
Study of Convolutional~ Neural Network Architectures}



\author[1]{Younes Ghazagh Jahed}%
\author[1]{Kamand Sarvari}%
\affil[1]{Affiliation not available}%


\vspace{-1em}



  
  \date{June 13, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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