Morphological metrics in galaxy images suffer systematic biases from resolution, depth, and noise that can be quantified and corrected empirically, with new metrics proposed to reduce those effects.
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2 Pith papers cite this work, alongside 6 external citations. Polarity classification is still indexing.
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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.
citing papers explorer
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statmorph-lsst: Quantifying and correcting morphological biases in galaxy surveys
Morphological metrics in galaxy images suffer systematic biases from resolution, depth, and noise that can be quantified and corrected empirically, with new metrics proposed to reduce those effects.
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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.