Deep Learning Models Achieve ~90% Accuracy in Galaxy Classification Task
Researchers compared ResNet101 and InceptionV4 neural networks for classifying galaxies in the Galaxy10 DECals dataset, finding both achieved approximately 90% accuracy. The study evaluated these architectures because they balance computational efficiency with the ability to process increasingly large astronomical image datasets. The results suggest either model could serve as a foundation for automated galaxy classification in future large-scale astronomical surveys.
A new study on arXiv evaluated two deep learning architectures—ResNet101 and InceptionV4—for classifying galaxy morphology in the Galaxy10 DECals dataset across ten galaxy classes. Both models achieved ~90% accuracy, consistent with prior published results. ResNet101 slightly outperformed InceptionV4 across measured performance metrics. The researchers chose these architectures because residual connections and inception modules enable deeper networks while maintaining computational efficiency, an important consideration as astronomical surveys generate exponentially larger image datasets. The authors conclude that either architecture could serve as a robust foundation for specialized pipelines in upcoming large-scale galaxy surveys.
What's missing
The study does not discuss potential limitations such as class imbalance effects, generalization to other galaxy datasets, or how performance varies across different galaxy morphology types. The paper also does not compare against other recent deep learning approaches (e.g., Vision Transformers) or provide ablation studies on specific architectural components.
What different sources said
- arXiv astro-phCenter
Classifying galaxies in the Galaxy10 DECals dataset using Inception and Residual CNNs
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