EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues A&A, 523 (2010) A31 Article references

Articles citing this article

The Citing articles tool gives a list of articles citing the current article.
The citing articles come from EDP Sciences database, as well as other publishers participating in CrossRef Cited-by Linking Program. You can set up your personal account to receive an email alert each time this article is cited by a new article (see the menu on the right-hand side of the abstract page).

Cited article:

The PAU survey

I. V. Daza-Perilla, M. Eriksen, D. Navarro-Gironés, E. J. Gonzalez, F. Rodriguez, E. Gaztañaga, C. M. Baugh, M. Lares, L. Cabayol-Garcia, F. J. Castander, M. Siudek, A. Wittje, H. Hildebrandt, R. Casas, P. Tallada-Crespí, J. Garcia-Bellido, E. Sanchez, I. Sevilla-Noarbe, R. Miquel, C. Padilla, P. Renard, J. Carretero and J. De Vicente
Astronomy & Astrophysics 693 A102 (2025)
https://doi.org/10.1051/0004-6361/202452053

Euclid preparation

I. Kovačić, M. Baes, A. Nersesian, N. Andreadis, L. Nemani, Abdurro’uf, L. Bisigello, M. Bolzonella, C. Tortora, A. van der Wel, S. Cavuoti, C. J. Conselice, A. Enia, L. K. Hunt, P. Iglesias-Navarro, E. Iodice, J. H. Knapen, F. R. Marleau, O. Müller, R. F. Peletier, J. Román, R. Ragusa, P. Salucci, T. Saifollahi, M. Scodeggio, et al.
Astronomy & Astrophysics 695 A284 (2025)
https://doi.org/10.1051/0004-6361/202453111

Performance of photometric template fitting for ultra-high-redshift galaxies

Thorbjørn Clausen, Charles L. Steinhardt, Arden Shao and Gaurav Senthil Kumar
Astronomy & Astrophysics 697 A160 (2025)
https://doi.org/10.1051/0004-6361/202453247

DAG Teleskobu için Üretilen Simülasyon Görüntüleriyle Fotometrik Kırmızıya Kaymaların Belirlenmesi

Süleyman Fişek and Sinan Aliş
Turkish Journal of Astronomy and Astrophysics 6 (1) 1 (2025)
https://doi.org/10.55064/tjaa.1613991

Photometric Selection of Type 1 Quasars in the XMM-LSS Field with Machine Learning and the Disk–Corona Connection

Jian Huang, Bin Luo, W. N. Brandt, Ying Chen, Qingling Ni, Yongquan Xue and Zijian Zhang
The Astrophysical Journal 979 (2) 107 (2025)
https://doi.org/10.3847/1538-4357/ad9baf

Exploring the physical properties of type II quasar candidates at intermediate redshifts with CIGALE

P. A. C. Cunha, A. Humphrey, J. Brinchmann, A. Paulino-Afonso, L. Bisigello, M. Bolzonella and D. Vaz
Astronomy & Astrophysics 696 A110 (2025)
https://doi.org/10.1051/0004-6361/202453068

ANNZ+: an enhanced photometric redshift estimation algorithm with applications on the PAU survey

Imdad Mahmud Pathi, John Y.H. Soo, Mao Jie Wee, Sazatul Nadhilah Zakaria, Nur Azwin Ismail, Carlton M. Baugh, Giorgio Manzoni, Enrique Gaztanaga, Francisco J. Castander, Martin Eriksen, Jorge Carretero, Enrique Fernandez, Juan Garcia-Bellido, Ramon Miquel, Cristobal Padilla, Pablo Renard, Eusebio Sanchez, Ignacio Sevilla-Noarbe and Pau Tallada-Crespí
Journal of Cosmology and Astroparticle Physics 2025 (01) 097 (2025)
https://doi.org/10.1088/1475-7516/2025/01/097

Hybrid-z: Enhancing the Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning

Anjitha John William, Priyanka Jalan, Maciej Bilicki, Wojciech A. Hellwing, Hareesh Thuruthipilly and Szymon J. Nakoneczny
Astronomy & Astrophysics 698 A276 (2025)
https://doi.org/10.1051/0004-6361/202453576

Dark Energy Survey Deep Field photometric redshift performance and training incompleteness assessment

L. Toribio San Cipriano, J. De Vicente, I. Sevilla-Noarbe, W. G. Hartley, J. Myles, A. Amon, G. M. Bernstein, A. Choi, K. Eckert, R. A. Gruendl, I. Harrison, E. Sheldon, B. Yanny, M. Aguena, S. S. Allam, O. Alves, D. Bacon, D. Brooks, A. Campos, A. Carnero Rosell, J. Carretero, F. J. Castander, C. Conselice, L. N. da Costa, M. E. S. Pereira, et al.
Astronomy & Astrophysics 686 A38 (2024)
https://doi.org/10.1051/0004-6361/202348956

The SRG/eROSITA All-Sky Survey: X-ray beacons at late cosmic dawn

J. Wolf, M. Salvato, S. Belladitta, R. Arcodia, S. Ciroi, F. Di Mille, T. Sbarrato, J. Buchner, S. Hämmerich, J. Wilms, W. Collmar, T. Dwelly, A. Merloni, T. Urrutia and K. Nandra
Astronomy & Astrophysics 691 A30 (2024)
https://doi.org/10.1051/0004-6361/202451035

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

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

Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows

John Franklin Crenshaw, J. Bryce Kalmbach, Alexander Gagliano, Ziang Yan, Andrew J. Connolly, Alex I. Malz and Samuel J. Schmidt
The Astronomical Journal 168 (2) 80 (2024)
https://doi.org/10.3847/1538-3881/ad54bf

UVCANDELS: Catalogs of Photometric Redshifts and Galaxy Physical Properties

Vihang Mehta, Marc Rafelski, Ben Sunnquist, Harry I. Teplitz, Claudia Scarlata, Xin Wang, Adriano Fontana, Nimish P. Hathi, Kartheik G. Iyer, Anahita Alavi, James Colbert, Norman Grogin, Anton Koekemoer, Kalina V. Nedkova, Matthew Hayes, Laura Prichard, Brian Siana, Brent M. Smith, Rogier Windhorst, Teresa Ashcraft, Micaela Bagley, Ivano Baronchelli, Guillermo Barro, Alex Blanche, Adam Broussard, et al.
The Astrophysical Journal Supplement Series 275 (1) 17 (2024)
https://doi.org/10.3847/1538-4365/ad7d8f

GAINN: The Galaxy Assembly and Interaction Neural Networks for High-redshift JWST Observations

Lillian Santos-Olmsted, Kirk S. S. Barrow and Tilman Hartwig
The Astrophysical Journal 969 (2) 144 (2024)
https://doi.org/10.3847/1538-4357/ad46fd

The Ultraviolet Luminosity Function at 0.6 < z < 1 from UVCANDELS

Lei Sun, Xin Wang, Harry I. Teplitz, Vihang Mehta, Anahita Alavi, Marc Rafelski, Rogier A. Windhorst, Claudia Scarlata, Jonathan P. Gardner, Brent M. Smith, Ben Sunnquist, Laura Prichard, Yingjie Cheng, Norman Grogin, Nimish P. Hathi, Matthew Hayes, Anton M. Koekemoer, Bahram Mobasher, Kalina V. Nedkova, Robert O’Connell, Brant Robertson, Sina Taamoli, L. Y. Aaron Yung, Gabriel Brammer, James Colbert, et al.
The Astrophysical Journal 972 (1) 8 (2024)
https://doi.org/10.3847/1538-4357/ad5540

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

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

Improved weak lensing photometric redshift calibration via StratLearn and hierarchical modelling

Maximilian Autenrieth, Angus H Wright, Roberto Trotta, David A van Dyk, David C Stenning and Benjamin Joachimi
Monthly Notices of the Royal Astronomical Society 534 (4) 3808 (2024)
https://doi.org/10.1093/mnras/stae2243

Euclid preparation

A. Enia, M. Bolzonella, L. Pozzetti, A. Humphrey, P. A. C. Cunha, W. G. Hartley, F. Dubath, S. Paltani, X. Lopez Lopez, S. Quai, S. Bardelli, L. Bisigello, S. Cavuoti, G. De Lucia, M. Ginolfi, A. Grazian, M. Siudek, C. Tortora, G. Zamorani, N. Aghanim, B. Altieri, A. Amara, S. Andreon, N. Auricchio, C. Baccigalupi, et al.
Astronomy & Astrophysics 691 A175 (2024)
https://doi.org/10.1051/0004-6361/202451425

Toward a stellar population catalog in the Kilo Degree Survey: The impact of stellar recipes on stellar masses and star formation rates

Linghua Xie, Nicola R. Napolitano, Xiaotong Guo, Crescenzo Tortora, Haicheng Feng, Antonios Katsianis, Rui Li, Sirui Wu, Mario Radovich, Leslie K. Hunt, Yang Wang, Lin Tang, Baitian Tang and Zhiqi Huang
Science China Physics, Mechanics & Astronomy 66 (12) (2023)
https://doi.org/10.1007/s11433-023-2173-8

Selection of powerful radio galaxies with machine learning

R. Carvajal, I. Matute, J. Afonso, R. P. Norris, K. J. Luken, P. Sánchez-Sáez, P. A. C. Cunha, A. Humphrey, H. Messias, S. Amarantidis, D. Barbosa, H. A. Cruz, H. Miranda, A. Paulino-Afonso and C. Pappalardo
Astronomy & Astrophysics 679 A101 (2023)
https://doi.org/10.1051/0004-6361/202245770

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

Potential scientific synergies in weak lensing studies between the CSST and Euclid space probes

D. Z. Liu, X. M. Meng, X. Z. Er, et al.
Astronomy & Astrophysics 669 A128 (2023)
https://doi.org/10.1051/0004-6361/202243978

Optimized Photometric Redshifts for the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS)

Dritan Kodra, Brett H. Andrews, Jeffrey A. Newman, Steven L. Finkelstein, Adriano Fontana, Nimish Hathi, Mara Salvato, Tommy Wiklind, Stijn Wuyts, Adam Broussard, Nima Chartab, Christopher Conselice, M. C. Cooper, Avishai Dekel, Mark Dickinson, Henry C. Ferguson, Eric Gawiser, Norman A. Grogin, Kartheik Iyer, Jeyhan Kartaltepe, Susan Kassin, Anton M. Koekemoer, David C. Koo, Ray A. Lucas, Kameswara Bharadwaj Mantha, et al.
The Astrophysical Journal 942 (1) 36 (2023)
https://doi.org/10.3847/1538-4357/ac9f12

First Impressions: Early-time Classification of Supernovae Using Host-galaxy Information and Shallow Learning

Alexander Gagliano, Gabriella Contardo, Daniel Foreman-Mackey, Alex I. Malz and Patrick D. Aleo
The Astrophysical Journal 954 (1) 6 (2023)
https://doi.org/10.3847/1538-4357/ace326

Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates

J. Singal, G. Silverman, E. Jones, T. Do, B. Boscoe and Y. Wan
The Astrophysical Journal 928 (1) 6 (2022)
https://doi.org/10.3847/1538-4357/ac53b5

Implications of a Temperature-dependent Initial Mass Function. II. An Updated View of the Star-forming Main Sequence

Charles L. Steinhardt, Albert Sneppen, Basel Mostafa, Hagan Hensley, Adam S. Jermyn, Adrian Lopez, John Weaver, Gabriel Brammer, Thomas H. Clark, Iary Davidzon, Andrei C. Diaconu, Bahram Mobasher, Vadim Rusakov and Sune Toft
The Astrophysical Journal 931 (1) 58 (2022)
https://doi.org/10.3847/1538-4357/ac62d6

Implications of a Temperature-dependent Initial Mass Function. I. Photometric Template Fitting

Albert Sneppen, Charles L. Steinhardt, Hagan Hensley, Adam S. Jermyn, Basel Mostafa and John R. Weaver
The Astrophysical Journal 931 (1) 57 (2022)
https://doi.org/10.3847/1538-4357/ac695e

Lensing without borders – I. A blind comparison of the amplitude of galaxy–galaxy lensing between independent imaging surveys

A Leauthaud, A Amon, S Singh, et al.
Monthly Notices of the Royal Astronomical Society 510 (4) 6150 (2022)
https://doi.org/10.1093/mnras/stab3586

Quasar photometric redshifts from incomplete data using deep learning

S J Curran
Monthly Notices of the Royal Astronomical Society 512 (2) 2099 (2022)
https://doi.org/10.1093/mnras/stac660

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

Spencer James Gibson, Aditya Narendra, Maria Giovanna Dainotti, et al.
Frontiers in Astronomy and Space Sciences 9 (2022)
https://doi.org/10.3389/fspas.2022.836215

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

WISE-PS1-STRM: neural network source classification and photometric redshifts for WISE×PS1

Róbert Beck, S Curt Dodds and István Szapudi
Monthly Notices of the Royal Astronomical Society 515 (4) 4711 (2022)
https://doi.org/10.1093/mnras/stac1714

Comparison of Observed Galaxy Properties with Semianalytic Model Predictions Using Machine Learning

Melanie Simet, Nima Chartab, Yu Lu and Bahram Mobasher
The Astrophysical Journal 908 (1) 47 (2021)
https://doi.org/10.3847/1538-4357/abd179

Predicting the Redshift of γ-Ray-loud AGNs Using Supervised Machine Learning

Maria Giovanna Dainotti, Malgorzata Bogdan, Aditya Narendra, Spencer James Gibson, Blazej Miasojedow, Ioannis Liodakis, Agnieszka Pollo, Trevor Nelson, Kamil Wozniak, Zooey Nguyen and Johan Larrson
The Astrophysical Journal 920 (2) 118 (2021)
https://doi.org/10.3847/1538-4357/ac1748

Nonsequential neural network for simultaneous, consistent classification, and photometric redshifts of OTELO galaxies

J. A. de Diego, J. Nadolny, Á. Bongiovanni, et al.
Astronomy & Astrophysics 655 A56 (2021)
https://doi.org/10.1051/0004-6361/202141360

Using a Neural Network Classifier to Select Galaxies with the Most Accurate Photometric Redshifts

Adam Broussard and Eric Gawiser
The Astrophysical Journal 922 (2) 153 (2021)
https://doi.org/10.3847/1538-4357/ac2147

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

Massimo Brescia, Stefano Cavuoti, Oleksandra Razim, et al.
Frontiers in Astronomy and Space Sciences 8 (2021)
https://doi.org/10.3389/fspas.2021.658229

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

Outlier Prediction and Training Set Modification to Reduce Catastrophic Outlier Redshift Estimates in Large-scale Surveys

M. Wyatt and J. Singal
Publications of the Astronomical Society of the Pacific 133 (1022) 044504 (2021)
https://doi.org/10.1088/1538-3873/abe5fb

Exploring New Redshift Indicators for Radio-Powerful AGN

Rodrigo Carvajal, Israel Matute, José Afonso, Stergios Amarantidis, Davi Barbosa, Pedro Cunha and Andrew Humphrey
Galaxies 9 (4) 86 (2021)
https://doi.org/10.3390/galaxies9040086

Dark Energy Survey Year 3 Results: clustering redshifts – calibration of the weak lensing source redshift distributions with redMaGiC and BOSS/eBOSS

M Gatti, G Giannini, G M Bernstein, et al.
Monthly Notices of the Royal Astronomical Society 510 (1) 1223 (2021)
https://doi.org/10.1093/mnras/stab3311

PS1-STRM: neural network source classification and photometric redshift catalogue for PS1 3π DR1

Róbert Beck, István Szapudi, Heather Flewelling, et al.
Monthly Notices of the Royal Astronomical Society 500 (2) 1633 (2020)
https://doi.org/10.1093/mnras/staa2587

See Change: VLT spectroscopy of a sample of high-redshift Type Ia supernova host galaxies

S C Williams, I M Hook, B Hayden, et al.
Monthly Notices of the Royal Astronomical Society 495 (4) 3859 (2020)
https://doi.org/10.1093/mnras/staa1319

Photometric Redshift Estimation with Galaxy Morphology Using Self-organizing Maps

Derek Wilson, Hooshang Nayyeri, Asantha Cooray and Boris Häußler
The Astrophysical Journal 888 (2) 83 (2020)
https://doi.org/10.3847/1538-4357/ab5a79

Redshift inference from the combination of galaxy colours and clustering in a hierarchical Bayesian model – Application to realistic N-body simulations

Alex Alarcon, Carles Sánchez, Gary M Bernstein and Enrique Gaztañaga
Monthly Notices of the Royal Astronomical Society 498 (2) 2614 (2020)
https://doi.org/10.1093/mnras/staa2478

Assessing the photometric redshift precision of the S-PLUS survey: the Stripe-82 as a test-case

A Molino, M V Costa-Duarte, L Sampedro, et al.
Monthly Notices of the Royal Astronomical Society 499 (3) 3884 (2020)
https://doi.org/10.1093/mnras/staa1586

A framework for measuring weak-lensing magnification using the Fundamental Plane

Jenna K C Freudenburg, Eric M Huff and Christopher M Hirata
Monthly Notices of the Royal Astronomical Society 496 (3) 2998 (2020)
https://doi.org/10.1093/mnras/staa1505

Galaxy clustering in the DESI Legacy Survey and its imprint on the CMB

Qianjun Hang, Shadab Alam, John A Peacock and Yan-Chuan Cai
Monthly Notices of the Royal Astronomical Society 501 (1) 1481 (2020)
https://doi.org/10.1093/mnras/staa3738

The PAU Survey: Photometric redshifts using transfer learning from simulations

M Eriksen, A Alarcon, L Cabayol, et al.
Monthly Notices of the Royal Astronomical Society 497 (4) 4565 (2020)
https://doi.org/10.1093/mnras/staa2265

Tests of Catastrophic Outlier Prediction in Empirical Photometric Redshift Estimation with Redshift Probability Distributions

E. Jones and J. Singal
Publications of the Astronomical Society of the Pacific 132 (1008) 024501 (2020)
https://doi.org/10.1088/1538-3873/ab54ed

Testing the accuracy of 3D-HST photometric redshift estimates as reference samples for deep weak lensing studies

S F Raihan, T Schrabback, H Hildebrandt, D Applegate and G Mahler
Monthly Notices of the Royal Astronomical Society 497 (2) 1404 (2020)
https://doi.org/10.1093/mnras/staa1949

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

Christopher J. Fluke and Colin Jacobs
WIREs Data Mining and Knowledge Discovery 10 (2) (2020)
https://doi.org/10.1002/widm.1349

Hawaii Two-0: high-redshift galaxy clustering and bias

Róbert Beck, Conor McPartland, Andrew Repp, David Sanders and István Szapudi
Monthly Notices of the Royal Astronomical Society 493 (2) 2318 (2020)
https://doi.org/10.1093/mnras/staa432

Photometric Redshifts with the LSST. II. The Impact of Near-infrared and Near-ultraviolet Photometry

Melissa L. Graham, Andrew J. Connolly, Winnie Wang, Samuel J. Schmidt, Christopher B. Morrison, Željko Ivezić, Sébastien Fabbro, Patrick Côté, Scott F. Daniel, R. Lynne Jones, Mario Jurić, Peter Yoachim and J. Bryce Kalmbach
The Astronomical Journal 159 (6) 258 (2020)
https://doi.org/10.3847/1538-3881/ab8a43

Cosmological Studies from Tomographic Weak Lensing Peak Abundances and Impacts of Photo-z Errors

Shuo Yuan, Chuzhong Pan, Xiangkun Liu, Qiao Wang and Zuhui Fan
The Astrophysical Journal 884 (2) 164 (2019)
https://doi.org/10.3847/1538-4357/ab40a5

Horizon-AGN virtual observatory – 1. SED-fitting performance and forecasts for future imaging surveys

A Slyz, M Salvato, H J McCracken, et al.
Monthly Notices of the Royal Astronomical Society 486 (4) 5104 (2019)
https://doi.org/10.1093/mnras/stz1054

Photometric redshifts for galaxies in the Spitzer Extragalactic Representative Volume Survey (SERVS)

Janine Pforr, Mattia Vaccari, Mark Lacy, et al.
Monthly Notices of the Royal Astronomical Society 483 (3) 3168 (2019)
https://doi.org/10.1093/mnras/sty3075

Redshift inference from the combination of galaxy colours and clustering in a hierarchical Bayesian model

Carles Sánchez and Gary M Bernstein
Monthly Notices of the Royal Astronomical Society 483 (2) 2801 (2019)
https://doi.org/10.1093/mnras/sty3222

H0LiCOW – X. Spectroscopic/imaging survey and galaxy-group identification around the strong gravitational lens system WFI 2033−4723

D Sluse, C E Rusu, C D Fassnacht, et al.
Monthly Notices of the Royal Astronomical Society 490 (1) 613 (2019)
https://doi.org/10.1093/mnras/stz2483

RELICS: Reionization Lensing Cluster Survey

Dan Coe, Brett Salmon, Maruša Bradač, Larry D. Bradley, Keren Sharon, Adi Zitrin, Ana Acebron, Catherine Cerny, Nathália Cibirka, Victoria Strait, Rachel Paterno-Mahler, Guillaume Mahler, Roberto J. Avila, Sara Ogaz, Kuang-Han Huang, Debora Pelliccia, Daniel P. Stark, Ramesh Mainali, Pascal A. Oesch, Michele Trenti, Daniela Carrasco, William A. Dawson, Steven A. Rodney, Louis-Gregory Strolger, Adam G. Riess, et al.
The Astrophysical Journal 884 (1) 85 (2019)
https://doi.org/10.3847/1538-4357/ab412b

Bayesian photometric redshifts of blended sources

Daniel M Jones and Alan F Heavens
Monthly Notices of the Royal Astronomical Society 483 (2) 2487 (2019)
https://doi.org/10.1093/mnras/sty3279

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

Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies

V Amaro, S Cavuoti, M Brescia, et al.
Monthly Notices of the Royal Astronomical Society 482 (3) 3116 (2019)
https://doi.org/10.1093/mnras/sty2922

KiDS+VIKING-450: A new combined optical and near-infrared dataset for cosmology and astrophysics

Angus H. Wright, Hendrik Hildebrandt, Konrad Kuijken, et al.
Astronomy & Astrophysics 632 A34 (2019)
https://doi.org/10.1051/0004-6361/201834879

Gaussian mixture models for blended photometric redshifts

Daniel M Jones and Alan F Heavens
Monthly Notices of the Royal Astronomical Society 490 (3) 3966 (2019)
https://doi.org/10.1093/mnras/stz2687

A Catalog of Photometric Redshift and the Distribution of Broad Galaxy Morphologies

Nicholas Paul, Nicholas Virag and Lior Shamir
Galaxies 6 (2) 64 (2018)
https://doi.org/10.3390/galaxies6020064

Improving photometric redshift estimation using GPz: size information, post processing, and improved photometry

Zahra Gomes, Matt J Jarvis, Ibrahim A Almosallam and Stephen J Roberts
Monthly Notices of the Royal Astronomical Society 475 (1) 331 (2018)
https://doi.org/10.1093/mnras/stx3187

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

Photometric redshifts for Hyper Suprime-Cam Subaru Strategic Program Data Release 1

Masayuki Tanaka, Jean Coupon, Bau-Ching Hsieh, Sogo Mineo, Atsushi J Nishizawa, Joshua Speagle, Hisanori Furusawa, Satoshi Miyazaki and Hitoshi Murayama
Publications of the Astronomical Society of Japan 70 (SP1) (2018)
https://doi.org/10.1093/pasj/psx077

Photometric redshifts for the next generation of deep radio continuum surveys – I. Template fitting

Kenneth J. Duncan, Michael J. I. Brown, Wendy L. Williams, et al.
Monthly Notices of the Royal Astronomical Society 473 (2) 2655 (2018)
https://doi.org/10.1093/mnras/stx2536

Dark Energy Survey Year 1 results: cross-correlation redshifts – methods and systematics characterization

M Gatti, P Vielzeuf, C Davis, et al.
Monthly Notices of the Royal Astronomical Society 477 (2) 1664 (2018)
https://doi.org/10.1093/mnras/sty466

Degradation analysis in the estimation of photometric redshifts from non-representative training sets

J D Rivera, B Moraes, A I Merson, et al.
Monthly Notices of the Royal Astronomical Society 477 (4) 4330 (2018)
https://doi.org/10.1093/mnras/sty880

Dark Energy Survey Year 1 Results: redshift distributions of the weak-lensing source galaxies

B Hoyle, D Gruen, G M Bernstein, et al.
Monthly Notices of the Royal Astronomical Society 478 (1) 592 (2018)
https://doi.org/10.1093/mnras/sty957

Dark Energy Survey Year 1 Results: calibration of redMaGiC redshift distributions in DES and SDSS from cross-correlations

R Cawthon, C Davis, M Gatti, et al.
Monthly Notices of the Royal Astronomical Society 481 (2) 2427 (2018)
https://doi.org/10.1093/mnras/sty2424

Data Analytics and Management in Data Intensive Domains

Massimo Brescia, Stefano Cavuoti, Valeria Amaro, et al.
Communications in Computer and Information Science, Data Analytics and Management in Data Intensive Domains 822 61 (2018)
https://doi.org/10.1007/978-3-319-96553-6_5