A ResNet-50 and BiLSTM multi-modal fusion network achieves 99.81% galaxy recall and 99.66% star recall on a CSST simulated dataset of 125,896 objects.
The Astronomical Journal , abstract =
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Optimizes ImageNet-pretrained AlexNet, UMAP, and a bagging multi-cluster voting scheme with K-means, Birch and Agg for unsupervised galaxy morphology classification, reporting improved stability and consistency with galaxy evolution expectations.
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A Multi-modal Fusion Network for Star-Galaxy Classification from CSST Simulated Datasets
A ResNet-50 and BiLSTM multi-modal fusion network achieves 99.81% galaxy recall and 99.66% star recall on a CSST simulated dataset of 125,896 objects.
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Robustness Analysis of USmorph: II. Optimizing Feature Extraction, Dimensionality Reduction, and Clustering for Unsupervised Galaxy Morphology Classification
Optimizes ImageNet-pretrained AlexNet, UMAP, and a bagging multi-cluster voting scheme with K-means, Birch and Agg for unsupervised galaxy morphology classification, reporting improved stability and consistency with galaxy evolution expectations.