PICZL. Image-based photo-z for AGN

We fully utilize the spatial flux distribution in the vicinity of each source to produce reliable photo-z. We introduce PICZL, a machine-learning algorithm leveraging an ensemble of convolutional neural networks. Utilizing a cross-channel approach, the algorithm integrates distinct SED features from images with those obtained from catalog-level data.

Full probability distributions are achieved via the integration of Gaussian mixture models. On a validation sample of 8098 AGN, PICZL achieves an accuracy {sigma}NMAD of 4.5% with an outlier fraction {eta} of 5.6%. These results significantly outperform previous attempts to compute accurate photo-z for AGN using machine learning.

We highlight that the model's performance depends on many variables, predominantly the depth of the data and associated photometric error. A thorough evaluation of these dependencies is presented in the paper. Our streamlined methodology maintains consistent performance across the entire survey area, when accounting for differing data quality.

The same approach can be adopted for future deep photometric surveys such as LSST and Euclid, showcasing its potential for wide-scale realization. With this paper, we release an updated photo-z (including errors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields.