A multi-parameter fuzzy classification using sigmoidal membership functions derived from Gaussian mixture models on SDSS data yields less contaminated red and green-valley samples with clearer physical trends than hard-boundary methods.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Transfer learning with a Zoobot CNN on SDSS DR18 data identifies 3,679 lopsided spiral galaxies at 87% test accuracy, with lopsided systems showing higher star formation, bluer colors, lower mass and concentration.
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A Multi-parameter Fuzzy Set Framework for Classifying Red, Blue, and Green Valley Galaxies
A multi-parameter fuzzy classification using sigmoidal membership functions derived from Gaussian mixture models on SDSS data yields less contaminated red and green-valley samples with clearer physical trends than hard-boundary methods.
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Identifying lopsidedness in spiral galaxies using a Deep Convolutional Neural Network
Transfer learning with a Zoobot CNN on SDSS DR18 data identifies 3,679 lopsided spiral galaxies at 87% test accuracy, with lopsided systems showing higher star formation, bluer colors, lower mass and concentration.