Automating Galaxy Image Classification in Galaxy Zoo: A Comparative Study of Deep Learning Models

This study compares the effectiveness of Artificial Neural Networks (ANNs) and Logistic Regression in classifying galaxy images from Galaxy Zoo 1. We propose Convolutional Neural Network (CNN) and Autoencoder models as potential solutions to mitigate the burden of manual classification. Both models are analyzed, and results reveal that ANN surpasses Transferred Learning Logistic Regression in terms of accuracy and runtime. Further investigation highlights the impact of activation functions, neuron count, hidden layers, and algorithm ensembling on ANN's classification performance. Additionally, we explore training time complexity reduction through learning rate, optimization algorithms, and batch size. The findings provide valuable insights for galaxy image classification tasks.