The many flavours of photometric redshifts

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

Maps of Dust Infrared Emission for Use in Estimation of Reddening and Cosmic Microwave Background Radiation Foregrounds

Schlegel, D. J., Finkbeiner, D. P., & Davis, M. (1998) · The Astrophysical Journal · doi:10.1086/305772 · Cited by 15,126

Stellar population synthesis at the resolution of 2003

Bruzual, G. A., & Charlot, S. (2003) · Monthly Notices of the Royal Astronomical Society · doi:10.1046/j.1365-8711.2003.06897.x · Cited by 10,505

SExtractor: Software for source extraction

Bertin, É., & Arnouts, S. (1996) · Astronomy & Astrophysics Supplement Series · doi:10.1051/aas:1996164 · Cited by 8,949

The Dust Content and Opacity of Actively Star‐forming Galaxies

Calzetti, D., Armus, L., Bohlin, R. C., Kinney, A. L., Koornneef, J., & Storchi‐Bergmann, T. (2000) · The Astrophysical Journal · doi:10.1086/308692 · Cited by 5,707

LSST: From Science Drivers to Reference Design and Anticipated Data Products

Ivezić, Ž., Kahn, S. M., Tyson, J. A., Abel, B., Acosta, E., Allsman, R., Alonso, D., AlSayyad, Y., Anderson, S. F., Andrew, J., et al. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab042c · Cited by 4,357

The Demography of Massive Dark Objects in Galaxy Centers

Magorrian, J., Tremaine, S. D., Richstone, D. O., Bender, R., Bower, G., Dressler, A. M., Faber, S. M., Gebhardt, K., Green, R. F., Grillmair, C. J., et al. (1998) · The Astronomical Journal · doi:10.1086/300353 · Cited by 3,986

Evolutionary population synthesis: models, analysis of the ingredients and application to high-z galaxies

Maraston, C. (2005) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2005.09270.x · Cited by 1,907

EAZY: A Fast, Public Photometric Redshift Code

Brammer, G. B., van Dokkum, P. G., & Coppi, P. (2008) · The Astrophysical Journal · doi:10.1086/591786 · Cited by 1,876

Physics of the Interstellar and Intergalactic Medium

Draine, B. T. (2011) · doi:10.1515/9781400839087 · Cited by 1,787

The ROSAT Raster Survey in the North-Ecliptic Pole Field: X-ray Catalogue and Optical IdentificationsPreprint

Hasinger, G., Freyberg, M. J., Hu, E. M., Waters, C. Z., Capak, P. L., Moneti, A., & McCracken, H. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.04718

The application of ridgelines in extended radio source cross-identification

Barkus, B., Croston, J. H., Piotrowska, J. M., Mingo, B., Best, P. N., Hardcastle, M. J., Mostert, R. I. J., Röttgering, H. J. A., Sabater, J., Webster, B., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2952

Developing a photometric estimate of quasar redshifts: An independent measurement of the distances to the most powerful objects in the Universe

Moss, J. (2019) · doi:10.26686/wgtn.17142089

Measuring photometric redshifts for high-redshift radio source surveys

Luken, K., Norris, R. P., Wang, X. R., Park, L. A. F., Guo, Y., & Filipović, M. D. (2023) · Publications of the Astronomical Society of Australia · doi:10.1017/pasa.2023.39

DESI Complete Calibration of the Color-Redshift Relation (DC3R2): Results from early DESI dataPreprint

McCullough, J. (2023) · arXiv (Cornell University) · doi:10.5281/zenodo.8328495

Surveys of the Cosmic X-ray Background

Brandt, W. N., & Yang, G. (2024) · Springer eBooks · doi:10.1007/978-981-19-6960-7_130

Recovered supernova Ia rate from simulated LSST images

Petrecca, V., Botticella, M. T., Cappellaro, E., Greggio, L., Sánchez, B., Möller, A., Sako, M., Graham, M. L., Paolillo, M., & Bianco, F. B. (2024) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361/202349012/pdf

Star formation as a function of environment in the MeerKAT Galaxy Clusters Legacy Survey

Kesebonye, K. C. (2023) · doi:10.29086/10413/22200

The PAU survey

Daza-Perilla, I. V., Eriksen, M., Navarro-Gironés, D., Gonzalez, E. J., Rodríguez, F., GAZTAÑAGA, E., Baugh, C. M., Lares, M., Cabayol-Garcia, L., Castander, F. J., et al. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202452053

DAG Teleskobu için Üretilen Simülasyon Görüntüleriyle Fotometrik Kırmızıya Kaymaların Belirlenmesi

Fişek, S., & Aliş, S. (2025) · Turkish Journal of Astronomy and Astrophysics · doi:10.55064/tjaa.1613991

Improving Optical Photo-z Constraints for Dusty Star-forming Galaxies Using Submillimeter-based Priors

Tanouri, P., Hill, R., Scott, D., & Chapin, E. L. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ade0af

Quasar candidate selection and redshift estimation using J-PLUS DR3 data

Yang, X.-Y., Li, C., Zhang, Y., Cui, C., Li, J., Zhang, J., Wei, S. L., Tang, C., & Wu, X.-B. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf1427

Determination of Galaxy Photometric Redshifts Using Conditional Generative Adversarial Networks (CGANs)

Garcia-Fernandez, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-08462-0_6

Beyond Templates: A Deep Learning Approach to Photometric Redshifts

Patel, M., & Shah, P. D. (2025) · International Conference on Information, Implementation, and Innovation in Technology (I2ITCON) · doi:10.1109/i2itcon65200.2025.11210631

TOPz: Photometric redshifts using template fitting applied to the GAMA survey

Tempel, E., Laur, J., Jones, Z. R., Kipper, R., Liivamägi, L. J., Pandey, D., Sakteos, G., Tamm, A., Triantafyllaki, A. N., & Tuvikene, T. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202553683

Black hole merger rates in the first billion years in light of JWST data

Cáceres-Burgos, P. F. V., Dayal, P., Lira, P., Mauerhofer, V., Pratama, F., & Trebitsch, M. (2026) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202556153

Photo- z Estimation with Normalizing Flow

Ren, Y., Chan, K. C., Zhang, L., Li, Y., Zhang, H., Song, R., Gong, Y., Meng, X., & Zhou, X. (2026) · The Astrophysical Journal · doi:10.3847/1538-4357/ae201d

The redshifts from 122 bands: Comparative redshift forecast for low-resolution spectra from SPHEREx and the 7-Dimensional Sky Survey (7DS)

Bae, J., Lee, B., Im, M., Bahk, H., Dachan, K., Hwang, H. S., Hong, S., Kim, S., Kim, M., Kim, T., et al. (2026) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202556999

Unveiling the Sources of X-Ray Luminosity in DESI Galaxy Groups: Insights from the SRG/eROSITA All-sky Survey

郑, Y.-L. 云. Z., Yang, X., 刘, T. 腾. L., 陈, S. 世. C., Bülbül, E., 刘, A. 昂. L., 张, Y. 艺. Z., 李, D. 大. L., 康, X. 熙. K., Gu, Y., et al. (2026) · The Astrophysical Journal · doi:10.3847/1538-4357/ae21d5

AI-driven reconstruction of large-scale structure from combined photometric and spectroscopic surveys: A test based on N -body simulations

Du, W., Luo, X., Jiang, Z., Xiao, X., Wang, X., Yin, F., Zhang, L., Li, X. Y., Lin, Q., & Wang, Y. O. (2026) · Physical review. D/Physical review. D. · doi:10.1103/9g7z-k92b

COSMOS-Web: estimating physical parameters of galaxies using Self-Organizing Maps

Abedini, F., Gozaliasl, G., Zonoozi, A. H., Kalantari, A., Korpi, M. J., Ilbert, O., Akins, H. B., Allen, N., Arango-Toro, R. C., Casey, C. M., et al. (2026) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stag337

Predicting Galaxy Redshifts Using Machine Learning Techniques

Kabra, I., & Ravindra, C. (2026) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc67433.2026.11393648

Study of the optical properties of X-ray selected galaxy clusters : multi-wavelengths analysis and implications for the future large surveys

Ricci, M. (2018) · doi:10.70675/47fd638az9347z482az8b0cz9e31855e43e6

The first galaxies reionizing the Universe as seen with JWST and COSMOS

Kauffmann, O. B. (2021) · doi:10.70675/e0f41239z734dz4310z905ez59953d3df01c

Magnifying the galaxy-halo connection in the Euclid Deep fields

Shuntov, M. (2022) · doi:10.70675/0c72b025z0553z42d3z876bz089ce2a6d906

Caractérisation d'un détecteur NIR et simulation des performances de la caméra CAGIRE pour le suivi des sursauts gamma détectés par la mission SVOM

de la Flèche, A. N. (2023) · doi:10.70675/167b8ecczf4a2z4609zbcd7z8e70377bfc76

Empreintes cosmologiques et gravitationnelles de la forme des halos de matière noire : Analyse statistique et apprentissage automatique

Koskas, R. (2024) · doi:10.70675/024c122fz3681z4bdez9d63zf411141028f4

Building Clean Galaxy Samples From SDSS: An Assessment of Galaxy Selection

Atalı, H., & ALİŞ, S. (2026) · Turkish Journal of Astronomy and Astrophysics · doi:10.55064/tjaa.1869809

Adversarial Attacks on Machine Learning Based Photometric Redshift EstimationPreprint

Kathale, S., & Pandit, P. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6573477

Search for quasar pairs with Gaia astrometric data

Zhu, X., Chen, Q., Liang, J., Deng, Z., Xia, J.-Q., Zhang, Y., & Wu, J. (2026) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202658953

Optimizing Deep Learning Photometric Redshifts for the Roman Space Telescope with HST/CANDELS

Khederlarian, A., Andrews, B. H., Newman, J. A., Zhang, T., & Dey, B. (2026) · The Astronomical Journal · doi:10.3847/1538-3881/ae5dc4

Weak Lensing Cosmology in the Large Survey Era

Fan, Z. (2026) · Research in Astronomy and Astrophysics · doi:10.1088/1674-4527/ae842c

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Dey, B., Newman, J. A., Zhang, T., Aguilar, J., Ahlen, S. P., Anand, A., Andrews, B. H., Bailey, S., Bianchi, D., Brooks, D., et al. (2026) · The Astronomical Journal · doi:10.3847/1538-3881/ae7441

Deep Submillimeter and Radio Observations of the SSA22 Field. IV. Spectral Energy Distributions, Star Formation Histories, and the Infrared–Radio Correlation of the 850 μ m–selected Submillimeter Galaxies

Zeng, X., Ao, Y., Mawatari, K., & Umehata, H. (2026) · The Astrophysical Journal · doi:10.3847/1538-4357/ae9611

Estimating redshifts via self-organizing maps and an early-stopping approach

Nucita, A., Paolis, F. D., Franco, A. A., Donatiello, G. V., Strafella, F., Bolzonella, M., & Cazzolla, M. (2026) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202659440

Selection-aware conformal photometric redshifts for Sloan Digital Sky Survey galaxies with AllWISE infrared constraints

Bai, Y., & Liu, J. (2026) · New Astronomy · doi:10.1016/j.newast.2026.102665

QAF: QNN with Astronomical Foundation Model-Based Framework for Photometric Redshift Estimation in Large Scale Surveys

Biswàs, S., Ghosh, S., Deshpande, S. S., & Pal, A. (2026) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss60577.2026.11731401

Appendices and photo-z catalogs in CLAP. I.

Lin, Q. (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.13954480

Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case

Brescia, M., Cavuoti, S., Razim, O., Amaro, V., Riccio, G., & Longo, G. (2021) · Frontiers in Astronomy and Space Sciences · doi:10.3389/fspas.2021.658229 · Cited by 33

Template-fitting meets deep learning: redshift estimation using physics-guided neural networks

Ferrao, J. C., Dias, D., Naik, P., D'Cruz, G., Naik, A., Khandeparkar, S., & Dessai, M. G. F. (2026) · Journal of Astrophysics and Astronomy · doi:10.1007/s12036-026-10144-5 · Cited by 1

Photometric redshifts and properties of galaxies from the sloan digital sky survey

Greisel, N. (2015) · doi:10.5282/edoc.18450

Photometric Redshifts: A Perspective from an Old-Timer 1 on Its Past, Present, and PotentialPreprint

Koo, D. C. (1999)

Overview of photometric redshift estimators.

Wang, D., Zhang, Y. X., & Zhao, Y. (2008) · Progress in Astronomy