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PICZL: Image-based photometric redshifts for AGN

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Fraction of broad absorption line quasars in different radio morphologies

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PhotoRedshift-MML: A multimodal machine learning method for estimating photometric redshifts of quasars

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Testing the evolutionary pathways of galaxies and their supermassive black holes and the impact of feedback from active galactic nuclei via large multiwavelength data sets

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Estimating galaxy redshift in radio-selected datasets using machine learning

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Astronomy and Computing 39 100557 (2022)
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A search for X-ray absorbed sources in the 3XMM catalogue using photometric redshifts and Bayesian spectral fits

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Astronomy & Astrophysics 645 A74 (2021)
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Automated algorithms to build active galactic nucleus classifiers

S Falocco, F J Carrera and J Larsson
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A Machine Learning Based Morphological Classification of 14,245 Radio AGNs Selected from the Best–Heckman Sample

Zhixian Ma, Haiguang Xu, Jie Zhu, Dan Hu, Weitian Li, Chenxi Shan, Zhenghao Zhu, Liyi Gu, Jinjin Li, Chengze Liu and Xiangping Wu
The Astrophysical Journal Supplement Series 240 (2) 34 (2019)
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Preliminary Results of Using k-nearest-neighbor Regression to Estimate the Redshift of Radio-selected Data Sets

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Photometric redshifts for X-ray-selected active galactic nuclei in the eROSITA era

M Brescia, M Salvato, S Cavuoti, et al.
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Measuring the Probabilistic Photometric Redshifts of X-ray Quasars Based on the Quantile Regression of Ensembles of Decision Trees

A. V. Meshcheryakov, V. V. Glazkova, S. V. Gerasimov and I. V. Mashechkin
Astronomy Letters 44 (12) 735 (2018)
https://doi.org/10.1134/S1063773718120058