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LSTM-MDNz: Estimating Quasar Photometric Redshifts with an LSTM-augmented Mixture Density Network
Jianzhen Chen, Zhijian Luo, Liping Fu, Zhu Chen, Hubing Xiao, Shaohua Zhang and Chenggang Shu The Astrophysical Journal Supplement Series 282(2) 46 (2026) https://doi.org/10.3847/1538-4365/ae2eb2
AGNBoost: A Machine Learning Approach to Active Galactic Nuclei Identification with JWST/NIRCam+MIRI Colors and Photometry
Kurt Hamblin, Allison Kirkpatrick, Bren E. Backhaus, Gregory Troiani, Jeyhan S. Kartaltepe, Dale D. Kocevski, Anton M. Koekemoer, Erini Lambrides, Casey Papovich, Kaila Ronayne, Guang Yang, Micaela B. Bagley, Mark Dickinson, Steven L. Finkelstein, Pablo Arrabal Haro, Fabio Pacucci, Jonathan R. Trump, Nor Pirzkal, Alexander de la Vega, Edgar Perez Vidal and L. Y. Aaron Yung The Astrophysical Journal 1002(2) 223 (2026) https://doi.org/10.3847/1538-4357/ae4d3f
DAG Teleskobu için Üretilen Simülasyon Görüntüleriyle Fotometrik Kırmızıya Kaymaların Belirlenmesi
QZO: A Catalog of 5 Million Quasars from the Zwicky Transient Facility
S. J. Nakoneczny, M. J. Graham, D. Stern, G. Helou, S. G. Djorgovski, E. C. Bellm, T. X. Chen, R. Dekany, A. Drake, A. A. Mahabal, T. A. Prince, R. Riddle, B. Rusholme and N. Sravan The Astrophysical Journal 992(1) 153 (2025) https://doi.org/10.3847/1538-4357/adf4e4
Euclid preparation
A. Humphrey, P. A. C. Cunha, L. Bisigello, C. Tortora, M. Bolzonella, L. Pozzetti, M. Baes, B. R. Granett, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, S. Bardelli, C. Bodendorf, D. Bonino, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, S. Casas, M. Castellano, et al. Astronomy & Astrophysics 702 A74 (2025) https://doi.org/10.1051/0004-6361/202452468
Semi-supervised classification of stars, galaxies and quasars using K-means and random-forest approaches
TOPz: Photometric redshifts using template fitting applied to the GAMA survey
E. Tempel, J. Laur, Z. R. Jones, R. Kipper, L. J. Liivamägi, D. Pandey, G. Sakteos, A. Tamm, A. N. Triantafyllaki and T. Tuvikene Astronomy & Astrophysics 703 A261 (2025) https://doi.org/10.1051/0004-6361/202553683
Euclid preparation
M. Selwood, S. Fotopoulou, M. N. Bremer, L. Bisigello, H. Landt, E. Bañados, G. Zamorani, F. Shankar, D. Stern, E. Lusso, L. Spinoglio, V. Allevato, F. Ricci, A. Feltre, F. Mannucci, M. Salvato, R. A. A. Bowler, M. Mignoli, D. Vergani, F. La Franca, A. Amara, S. Andreon, N. Auricchio, M. Baldi, S. Bardelli, et al. Astronomy & Astrophysics 693 A250 (2025) https://doi.org/10.1051/0004-6361/202450894
Imputation of missing photometric data and photometric redshift estimation for CSST
Zhijian Luo, Zhirui Tang, Zhu Chen, Liping Fu, Wei Du, Shaohua Zhang, Yan Gong, Chenggang Shu, Junhao Lu, Yicheng Li, Xian-Min Meng, Xingchen Zhou and Zuhui Fan Monthly Notices of the Royal Astronomical Society 531(3) 3539 (2024) https://doi.org/10.1093/mnras/stae1397
PICZL: Image-based photometric redshifts for AGN
W. Roster, M. Salvato, S. Krippendorf, A. Saxena, R. Shirley, J. Buchner, J. Wolf, T. Dwelly, F. E. Bauer, J. Aird, C. Ricci, R. J. Assef, S. F. Anderson, X. Liu, A. Merloni, J. Weller and K. Nandra Astronomy & Astrophysics 692 A260 (2024) https://doi.org/10.1051/0004-6361/202452361
Machine learning applications in studies of the physical properties of active galactic nuclei based on photometric observations
Photometric redshift estimation for CSST survey with LSTM neural networks
Zhijian Luo, Yicheng Li, Junhao Lu, Zhu Chen, Liping Fu, Shaohua Zhang, Hubing Xiao, Wei Du, Yan Gong, Chenggang Shu, Wenwen Ma, Xianmin Meng, Xingchen Zhou and Zuhui Fan Monthly Notices of the Royal Astronomical Society 535(2) 1844 (2024) https://doi.org/10.1093/mnras/stae2446
Fine-grained photometric classification using multi-model fusion method with redshift estimation
Improving machine learning-derived photometric redshifts and physical property estimates using unlabelled observations
A Humphrey, P A C Cunha, A Paulino-Afonso, et al. Monthly Notices of the Royal Astronomical Society 520(1) 305 (2023) https://doi.org/10.1093/mnras/stac3596
Machine learning-based photometric classification of galaxies, quasars, emission-line galaxies, and stars
Fatemeh Zahra Zeraatgari, Fatemeh Hafezianzadeh, Yanxia Zhang, Liquan Mei, Ashraf Ayubinia, Amin Mosallanezhad and Jingyi Zhang Monthly Notices of the Royal Astronomical Society 527(3) 4677 (2023) https://doi.org/10.1093/mnras/stad3436
Estimating photometric redshift from mock flux for CSST survey by using weighted Random Forest
Junhao Lu, Zhijian Luo, Zhu Chen, Liping Fu, Wei Du, Yan Gong, Yicheng Li, Xian-Min Meng, Zhirui Tang, Shaohua Zhang, Chenggang Shu, Xingchen Zhou and Zuhui Fan Monthly Notices of the Royal Astronomical Society 527(4) 12140 (2023) https://doi.org/10.1093/mnras/stad3976
Data-driven templates with dictionary learning and sparse representations for photometric redshift estimation
Predicting the Redshift of Gamma-Ray Loud AGNs Using Supervised Machine Learning. II
Aditya Narendra, Spencer James Gibson, Maria Giovanna Dainotti, Malgorzata Bogdan, Agnieszka Pollo, Ioannis Liodakis, Artem Poliszczuk and Enrico Rinaldi The Astrophysical Journal Supplement Series 259(2) 55 (2022) https://doi.org/10.3847/1538-4365/ac545a
Using Multivariate Imputation by Chained Equations to Predict Redshifts of Active Galactic Nuclei
Identifying and Repairing Catastrophic Errors in Galaxy Properties Using Dimensionality Reduction
Beryl Hovis-Afflerbach, Charles L. Steinhardt, Daniel Masters and Mara Salvato The Astrophysical Journal 908(2) 148 (2021) https://doi.org/10.3847/1538-4357/abd329
The effect of phased recurrent units in the classification of multiple catalogues of astronomical light curves
C Donoso-Oliva, G Cabrera-Vives, P Protopapas, R Carrasco-Davis and P A Estevez Monthly Notices of the Royal Astronomical Society 505(4) 6069 (2021) https://doi.org/10.1093/mnras/stab1598
AstronomicAL: an interactive dashboard for visualisation, integration and classification of data with Active Learning
Grant Stevens, Sotiria Fotopoulou, Malcolm Bremer and Oliver Ray Journal of Open Source Software 6(65) 3635 (2021) https://doi.org/10.21105/joss.03635
Augmenting machine learning photometric redshifts with Gaussian mixture models
P W Hatfield, I A Almosallam, M J Jarvis, N Adams, R A A Bowler, Z Gomes, S J Roberts and C Schreiber Monthly Notices of the Royal Astronomical Society 498(4) 5498 (2020) https://doi.org/10.1093/mnras/staa2741
Catalogues of active galactic nuclei from Gaia and unWISE data
Yiping Shu, Sergey E Koposov, N Wyn Evans, et al. Monthly Notices of the Royal Astronomical Society 489(4) 4741 (2019) https://doi.org/10.1093/mnras/stz2487
Horizon-AGN virtual observatory – 1. SED-fitting performance and forecasts for future imaging surveys
A Comparison of Photometric Redshift Techniques for Large Radio Surveys
Ray P. Norris, M. Salvato, G. Longo, et al. Publications of the Astronomical Society of the Pacific 131(1004) 108004 (2019) https://doi.org/10.1088/1538-3873/ab0f7b
Photometric redshifts for X-ray-selected active galactic nuclei in the eROSITA era