A multimodal neural network trained on MPA-JHU references produces SFR, stellar mass, and metallicity estimates for 547 million low-redshift galaxies in DESI LS DR10.
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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.
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A Value-added Physical Properties Catalog for Low-redshift Galaxies from DESI Legacy Imaging Surveys DR10
A multimodal neural network trained on MPA-JHU references produces SFR, stellar mass, and metallicity estimates for 547 million low-redshift galaxies in DESI LS DR10.
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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.