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.
F., Argudo-Fernández, M., et al
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Machine learning models achieve NMAD 0.036 and 5.6% outliers for quasar photometric redshifts, identifying 185 high-probability pair candidates in MGQPC with 20 spectroscopically confirmed as physical pairs.
Green valley galaxies live preferentially in the outskirts of galaxy clusters as satellites, consistent with environmental quenching processes.
citing papers explorer
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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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Search for quasar pairs with Gaia astrometric data. II. Photometric redshift prediction with machine learning for the MGQPC catalogue
Machine learning models achieve NMAD 0.036 and 5.6% outliers for quasar photometric redshifts, identifying 185 high-probability pair candidates in MGQPC with 20 spectroscopically confirmed as physical pairs.
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Cluster-green galaxy correlations: where do these galaxies live?
Green valley galaxies live preferentially in the outskirts of galaxy clusters as satellites, consistent with environmental quenching processes.