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Improving Photometric Redshift Estimation for CSST Mock Catalog Using SED Templates Calibrated with Perturbation Algorithm
Yicheng Li, Liping Fu, Zhu Chen, Zhijian Luo, Wei Du, Yan Gong, Xianmin Meng, Junhao Lu, Zhirui Tang, Pengfei Chen, Shaohua Zhang, Chenggang Shu, Xingchen Zhou and Zuhui Fan Research in Astronomy and Astrophysics 25(5) 055021 (2025) https://doi.org/10.1088/1674-4527/adcc7e
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
CLAP
Qiufan Lin, Hengxin Ruan, Dominique Fouchez, Shupei Chen, Rui Li, Paulo Montero-Camacho, Nicola R. Napolitano, Yuan-Sen Ting and Wei Zhang Astronomy & Astrophysics 691 A331 (2024) https://doi.org/10.1051/0004-6361/202349113
Redshift Prediction with Images for Cosmology Using a Bayesian Convolutional Neural Network with Conformal Predictions
Evan Jones, Tuan Do, Yun Qi Li, Kevin Alfaro, Jack Singal and Bernie Boscoe The Astrophysical Journal 974(2) 159 (2024) https://doi.org/10.3847/1538-4357/ad6d5a
Testing the transferability of machine learning techniques for determining photometric redshifts of galaxy catalogue populations
Lara Janiurek, Martin A Hendry and Fiona C Speirits Monthly Notices of the Royal Astronomical Society 533(3) 2786 (2024) https://doi.org/10.1093/mnras/stae1901
hayate: photometric redshift estimation by hybridizing machine learning with template fitting
Shingo Tanigawa, K Glazebrook, C Jacobs, I Labbe and A K Qin Monthly Notices of the Royal Astronomical Society 530(2) 2012 (2024) https://doi.org/10.1093/mnras/stae411
The PAU survey: photometric redshift estimation in deep wide fields
D Navarro-Gironés, E Gaztañaga, M Crocce, A Wittje, H Hildebrandt, A H Wright, M Siudek, M Eriksen, S Serrano, P Renard, E J Gonzalez, C M Baugh, L Cabayol, J Carretero, R Casas, F J Castander, I V Daza-Perilla, J De Vicente, E Fernandez, J García-Bellido, H Hoekstra, G Manzoni, R Miquel, C Padilla, E Sánchez, et al. Monthly Notices of the Royal Astronomical Society 534(2) 1504 (2024) https://doi.org/10.1093/mnras/stae1686
Improving Photometric Redshift Estimation for Cosmology with LSST Using Bayesian Neural Networks
Evan Jones, Tuan Do, Bernie Boscoe, Jack Singal, Yujie Wan and Zooey Nguyen The Astrophysical Journal 964(2) 130 (2024) https://doi.org/10.3847/1538-4357/ad2070
SpyderZ: An Efficient Support Vector Machine Library for Photometric Redshift Estimation and Redshift Probability Information
Photometric redshift estimation of quasars with fused features from photometric data and images
Lin Yao, Bo Qiu, A-Li Luo, Jianwei Zhou, Kuang Wu, Xiao Kong, Yuanbo Liu, Guiyu Zhao and Kun Wang Monthly Notices of the Royal Astronomical Society 523(4) 5799 (2023) https://doi.org/10.1093/mnras/stad1842
Photometric redshifts from SDSS images with an interpretable deep capsule network
Biprateep Dey, Brett H Andrews, Jeffrey A Newman, Yao-Yuan Mao, Markus Michael Rau and Rongpu Zhou Monthly Notices of the Royal Astronomical Society 515(4) 5285 (2022) https://doi.org/10.1093/mnras/stac2105
Detection of extragalactic Ultra-compact dwarfs and Globular Clusters using Explainable AI techniques
Pattern Recognition Using SVM for the Classification of the Size and Distance of Trans-Neptunian Objects Detected by Serendipitous Stellar Occultations
B. Hernández-Valencia, J. H. Castro-Chacón, M. Reyes-Ruiz, et al. Publications of the Astronomical Society of the Pacific 134(1038) 084501 (2022) https://doi.org/10.1088/1538-3873/ac7f5c
Photometric redshift estimation with convolutional neural networks and galaxy images: Case study of resolving biases in data-driven methods
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
Estimating galaxy redshift in radio-selected datasets using machine learning
Morpho-z: improving photometric redshifts with galaxy morphology
John Y H Soo, Bruno Moraes, Benjamin Joachimi, et al. Monthly Notices of the Royal Astronomical Society 475(3) 3613 (2018) https://doi.org/10.1093/mnras/stx3201
The Radio Synchrotron Background: Conference Summary and Report