VAR-PZnn combines ZTF g-band variability features with optical, MIR and NIR photometry in a mixture-density network to estimate AGN photometric redshifts with σ_NMAD=0.058 and an 8.2% outlier fraction on a 72,728-source spectroscopic sample.
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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features
VAR-PZnn combines ZTF g-band variability features with optical, MIR and NIR photometry in a mixture-density network to estimate AGN photometric redshifts with σ_NMAD=0.058 and an 8.2% outlier fraction on a 72,728-source spectroscopic sample.