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Quasar photometric redshifts from incomplete data using deep learning · DOI 10.1093/mnras/stac660

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astro-ph.GA 1

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2026 1

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CONDITIONAL 1

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Predicting Redshift in Seyfert Galaxies Using Machine Learning

astro-ph.GA · 2026-04-20 · conditional · novelty 4.0

Random Forest regression on combined optical plus mid-infrared colors yields NMAD of 0.0188, R-squared of 0.9561, and 0.294 percent outliers for photometric redshifts in 23,797 Seyfert II galaxies selected from SDSS and WISE.

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  • Predicting Redshift in Seyfert Galaxies Using Machine Learning astro-ph.GA · 2026-04-20 · conditional · none · ref 33

    Random Forest regression on combined optical plus mid-infrared colors yields NMAD of 0.0188, R-squared of 0.9561, and 0.294 percent outliers for photometric redshifts in 23,797 Seyfert II galaxies selected from SDSS and WISE.