The many flavours of photometric redshifts

Random Forests

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

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 · see citing works

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 · see citing works

SExtractor: Software for source extraction

Bertin, É., & Arnouts, S. (1996) · Astronomy & Astrophysics Supplement Series · doi:10.1051/aas:1996164 · Cited by 8,937 · see citing works

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 · see citing works

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 · see citing works

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 4,007 · see citing works

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 · see citing works

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 · see citing works

Physics of the Interstellar and Intergalactic Medium

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

Euclid Definition Study Report

Laureijs, R., Amiaux, J., Arduini, S., Auguères, J., Brinchmann, J., Cole, R., Cropper, M., Dabin, C., Duvet, L., Ealet, A., et al. (2011) · CERN Document Server (European Organization for Nuclear Research) · doi:10.48550/arxiv.1110.3193 · Cited by 1,767 · see citing works

Accurate photometric redshifts for the CFHT legacy survey calibrated using the VIMOS VLT deep survey

Ilbert, O., Arnouts, S., McCracken, H. J., Bolzonella, M., Bertin, E., Fèvre, O. C. L., Mellier, Y., Zamorani, G., Pelló, R., Iovino, A., et al. (2006) · Astronomy and Astrophysics · doi:10.1051/0004-6361:20065138 · Cited by 1,683 · see citing works

UV LUMINOSITY FUNCTIONS AT REDSHIFTS z ∼ 4 TO z ∼ 10: 10,000 GALAXIES FROM HST LEGACY FIELDS

Bouwens, R. J., Illingworth, G. D., Oesch, P. A., Trenti, M., Labbé, I., Bradley, L., Carollo, C. M., van Dokkum, P. G., Gonzalez, V., Holwerda, B. W., et al. (2015) · The Astrophysical Journal · doi:10.1088/0004-637x/803/1/34 · Cited by 1,298 · see citing works

COSMOS PHOTOMETRIC REDSHIFTS WITH 30-BANDS FOR 2-deg 2

Ilbert, O., Capak, P. L., Salvato, M., Aussel, H., McCracken, H. J., Sanders, D. B., Scoville, N., Kartaltepe, J. S., Arnouts, S., Floc'h, E. L., et al. (2008) · The Astrophysical Journal · doi:10.1088/0004-637x/690/2/1236 · Cited by 1,258 · see citing works

Colors and magnitudes predicted for high redshift galaxies

Coleman, G. D., Wu, C., & Weedman, D. W. (1980) · The Astrophysical Journal Supplement Series · doi:10.1086/190674 · Cited by 1,223 · see citing works

Radiative transfer in a clumpy universe: The colors of high-redshift galaxies

Madau, P. (1995) · The Astrophysical Journal · doi:10.1086/175332 · Cited by 1,183 · see citing works

THE COSMOS2015 CATALOG: EXPLORING THE 1 < z < 6 UNIVERSE WITH HALF A MILLION GALAXIES

Laigle, C., McCracken, H. J., Ilbert, O., Hsieh, B.-C., Davidzon, I., Capak, P. L., Hasinger, G., Silverman, J. D., Pichon, C., Coupon, J., et al. (2016) · The Astrophysical Journal Supplement Series · doi:10.3847/0067-0049/224/2/24 · Cited by 1,145 · see citing works

Cosmology and fundamental physics with the Euclid satellite

The Euclid Theory Working Group, Amendola, L., Appleby, S. A., Avgoustidis, A., Bacon, D., Baker, T., Baldi, M., Bartolo, N., Blanchard, A., Bonvin, C., et al. (2018) · Living Reviews in Relativity · doi:10.1007/s41114-017-0010-3 · Cited by 1,002 · see citing works

Measuring and modelling the redshift evolution of clustering: the Hubble Deep Field North

Arnouts, S., Cristiani, S., Moscardini, L., Matarrese, S., Lucchin, F., Fontana, A., & Giallongo, E. (1999) · Monthly Notices of the Royal Astronomical Society · doi:10.1046/j.1365-8711.1999.02978.x · Cited by 982 · see citing works

Bayesian Photometric Redshift Estimation

Benı́tez, N. (1998) · The Astrophysical Journal · doi:10.1086/308947 · Cited by 974 · see citing works

Template Ultraviolet to Near-Infrared Spectra of Star-forming Galaxies and Their Application to K-Corrections

Kinney, A. L., Calzetti, D., Bohlin, R. C., McQuade, K., Storchi‐Bergmann, T., & Schmitt, H. R. (1996) · The Astrophysical Journal · doi:10.1086/177583 · Cited by 926 · see citing works

Cosmology and Fundamental Physics with the Euclid Satellite

The Euclid Theory Working Group, Amendola, L., Appleby, S., Bacon, D., Baker, T., Baldi, M., Bartolo, N., Blanchard, A., Bonvin, C., Borgani, S., et al. (2013) · Living Reviews in Relativity · doi:10.12942/lrr-2013-6 · Cited by 899 · see citing works

Spectral Energy Distributions of Hard X‐Ray Selected Active Galactic Nuclei in the XMM‐Newton Medium Deep Survey

Polletta, M. D. C., Tajer, M., Maraschi, L., Trinchieri, G., Lonsdale, C. J., Chiappetti, L., Andreon, S., Pierre, M., Fèvre, O. C. L., Zamorani, G., et al. (2007) · The Astrophysical Journal · doi:10.1086/518113 · Cited by 868 · see citing works

The Dark Energy Survey: more than dark energy - an overview

Abbott, D., Abdalla, F., Aleksic, J., Allam, S., Amara, A., Bacon, D., Balbinot, E., Banerji, M., Bechtol, K., Benoit-Lévy, A., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw641 · Cited by 766 · see citing works

The Hyper Suprime-Cam SSP Survey: Overview and survey design

Aihara, H., Arimoto, N., Armstrong, R., Arnouts, S., Bahcall, N. A., Bickerton, S. J., Bosch, J., Bundy, K. A., Capak, P. L., Chan, J. H. H., et al. (2018) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 749 · see citing works

COSMOS Photometric Redshifts with 30-bands for 2-deg2Preprint

Ilbert, O., Capak, P., Salvato, M., et al. (2008) · HAL (Le Centre pour la Communication Scientifique Directe) · doi:10.48550/arxiv.0809.2101 · Cited by 708 · see citing works

ANNz: Estimating Photometric Redshifts Using Artificial Neural Networks

Collister, A. A., & Lahav, O. (2004) · Publications of the Astronomical Society of the Pacific · doi:10.1086/383254 · Cited by 498 · see citing works

THE NEWFIRM MEDIUM-BAND SURVEY: PHOTOMETRIC CATALOGS, REDSHIFTS, AND THE BIMODAL COLOR DISTRIBUTION OF GALAXIES OUT TO z ∼ 3

Whitaker, K. E., Labbé, I., van Dokkum, P. G., Brammer, G., Kriek, M., Marchesini, D., Quadri, R. F., Franx, M., Muzzin, A., Williams, R. J., et al. (2011) · The Astrophysical Journal · doi:10.1088/0004-637x/735/2/86 · Cited by 497 · see citing works

EMU: Evolutionary Map of the Universe

Norris, R. P., Hopkins, A. M., Afonso, J., Brown, S., Condon, J. J., Dunne, L., Feain, I., Hollow, R. P., Jarvis, M., Johnston-Hollitt, M., et al. (2011) · Publications of the Astronomical Society of Australia · doi:10.1071/as11021 · Cited by 425 · see citing works

Power Spectrum Tomography with Weak Lensing

Hu, W. (1999) · The Astrophysical Journal · doi:10.1086/312210 · Cited by 420 · see citing works

A catalogue of the Chandra Deep Field South with multi-colour classification and photometric redshifts from COMBO-17

Wolf, C., Meisenheimer, K., Kleinheinrich, M., Borch, A., Dye, S., Gray, M. E., Wisotzki, L., Bell, E. F., Rix, H., Cimatti, A., et al. (2004) · Astronomy and Astrophysics · doi:10.1051/0004-6361:20040525 · Cited by 416 · see citing works

The VIMOS VLT Deep Survey - First epoch VVDS-Deep survey: 11564 spectra with 17.5<=IAB<=24, and the redshift distribution over 0< z <=5

Fèvre, O. C. L., Vettolani, G., Garilli, B., Tresse, L., Bottini, D., Brun, V. L., Maccagni, D., Picat, J. P., Scaramella, R., Scodeggio, M., et al. (2005) · HAL · doi:10.1051/0004-6361:20041960 · Cited by 393 · see citing works

PHOTOMETRIC REDSHIFT AND CLASSIFICATION FOR THEXMM-COSMOS SOURCES

Salvato, M., Hasinger, G., Ilbert, O., Zamorani, G., Brusa, M., Scoville, N. Z., Rau, A., Capak, P. L., Arnouts, S., Aussel, H., et al. (2008) · The Astrophysical Journal · doi:10.1088/0004-637x/690/2/1250 · Cited by 375 · see citing works

A CRITICAL ASSESSMENT OF PHOTOMETRIC REDSHIFT METHODS: A CANDELS INVESTIGATION

Dahlen, T., Mobasher, B., Faber, S. M., Ferguson, H. C., Barro, G., Finkelstein, S. L., Finlator, K., Fontana, A., Gruetzbauch, R., Johnson, S., et al. (2013) · The Astrophysical Journal · doi:10.1088/0004-637x/775/2/93 · Cited by 364 · see citing works

THE CHANDRA COSMOS LEGACY SURVEY: OPTICAL/IR IDENTIFICATIONS

Marchesi, S., Civano, F., Elvis, M., Salvato, M., Brusa, M., Comastri, A., Gilli, R., Hasinger, G., Lanzuisi, G., Miyaji, T., et al. (2016) · The Astrophysical Journal · doi:10.3847/0004-637x/817/1/34 · Cited by 340 · see citing works

THEXMM-NEWTONWIDE-FIELD SURVEY IN THE COSMOS FIELD (XMM-COSMOS): DEMOGRAPHY AND MULTIWAVELENGTH PROPERTIES OF OBSCURED AND UNOBSCURED LUMINOUS ACTIVE GALACTIC NUCLEI

Brusa, M., Civano, F., Comastri, A., Miyaji, T., Salvato, M., Zamorani, G., Cappelluti, N., Fiore, F., Hasinger, G., Mainieri, V., et al. (2010) · The Astrophysical Journal · doi:10.1088/0004-637x/716/1/348 · Cited by 334 · see citing works

Frank Rosenblatt: Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms

Malsburg, C. V. D. (1986) · Springer eBooks · doi:10.1007/978-3-642-70911-1_20 · Cited by 305 · see citing works

The XMM‐Newton Wide‐Field Survey in the COSMOS Field: Statistical Properties of Clusters of Galaxies

Finoguenov, A., Guzzo, L., Hasinger, G., Scoville, N. Z., Aussel, H., Böhringer, H., Brusa, M., Capak, P. L., Cappelluti, N., Comastri, A., et al. (2007) · The Astrophysical Journal Supplement Series · doi:10.1086/516577 · Cited by 301 · see citing works

The K20 survey

Cimatti, A., Daddi, E., Mignoli, M., Pozzetti, L., Renzini, A., Zamorani, G., Broadhurst, T. J., Fontana, A., Saracco, P., Poli, F., et al. (2002) · Astronomy and Astrophysics · doi:10.1051/0004-6361:20011696 · Cited by 289 · see citing works

THE SPECTRAL ENERGY DISTRIBUTIONS OF z ∼ 8 GALAXIES FROM THE IRAC ULTRA DEEP FIELDS: EMISSION LINES, STELLAR MASSES, AND SPECIFIC STAR FORMATION RATES AT 650 MYR

Labbé, I., Oesch, P. A., Bouwens, R. J., Illingworth, G. D., Magee, D. K., González, V., Carollo, C. M., Franx, M., Trenti, M., van Dokkum, P., et al. (2013) · The Astrophysical Journal Letters · doi:10.1088/2041-8205/777/2/l19 · Cited by 288 · see citing works

Calibrating Redshift Distributions beyond Spectroscopic Limits with Cross‐Correlations

Newman, J. A. (2008) · The Astrophysical Journal · doi:10.1086/589982 · Cited by 285 · see citing works

Dark energy survey year 1 results: photometric data set for cosmology

Lima, M. V. B. T. (2017) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · doi:10.3847/1538-4365/aab4f5/meta · Cited by 278 · see citing works

ANNz: estimating photometric redshifts using artificial neural networksPreprint

Collister, A., & Lahav, O. (2003) · doi:10.48550/arxiv.astro-ph/0311058 · Cited by 275 · see citing works

TPZ: photometric redshift PDFs and ancillary information by using prediction trees and random forests

Kind, M. C., & Brunner, R. J. (2013) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stt574 · Cited by 273 · see citing works

THE MULTIWAVELENGTH SURVEY BY YALE–CHILE (MUSYC): DEEP MEDIUM-BAND OPTICAL IMAGING AND HIGH-QUALITY 32-BAND PHOTOMETRIC REDSHIFTS IN THE ECDF-S

Cardamone, C. N., van Dokkum, P. G., Urry, C. M., Taniguchi, Y., Gawiser, E., Brammer, G., Taylor, E., Damen, M., Treister, E., Cobb, B. E., et al. (2010) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/189/2/270 · Cited by 273 · see citing works

PHAT: PHoto-zAccuracy Testing

Hildebrandt, H., Arnouts, S., Capak, P. L., Moustakas, L. A., Wolf, C., Abdalla, F. B., Assef, R. J., Banerji, M., Benítez, N., Brammer, G. B., et al. (2010) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201014885 · Cited by 270 · see citing works

The Zurich Extragalactic Bayesian Redshift Analyzer and its first application: COSMOS

Feldmann, R., Carollo, C. M., Porciani, C., Lilly, S. J., Capak, P. L., Taniguchi, Y., Fèvre, O. C. L., Renzini, A., Scoville, N. Z., Ajiki, M., et al. (2006) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2006.10930.x · Cited by 268 · see citing works

DISSECTING PHOTOMETRIC REDSHIFT FOR ACTIVE GALACTIC NUCLEUS USINGXMM- ANDCHANDRA-COSMOS SAMPLES

Salvato, M., Ilbert, O., Hasinger, G., Rau, A., Civano, F., Zamorani, G., Brusa, M., Elvis, M., Vignali, C., Aussel, H., et al. (2011) · The Astrophysical Journal · doi:10.1088/0004-637x/742/2/61 · Cited by 268 · see citing works

Estimating the redshift distribution of photometric galaxy samples – II. Applications and tests of a new method

Lima, M., Cunha, C., Oyaizu, H., Frieman, J., Lin, H., & Sheldon, E. (2008) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2008.13510.x · Cited by 256 · see citing works

CFHTLenS: Improving the quality of photometric redshifts with precision photometry

Hildebrandt, H., Erben, T., Kuijken, K., van Waerbeke, L., Heymans, C., Coupon, J., Benjamin, J., Bonnett, C., Fu, L., Hoekstra, H., et al. (2011) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2012.20468.x · Cited by 254 · see citing works

Accreting supermassive black holes in the COSMOS field and the connection to their host galaxies

Bongiorno, A., Merloni, A., Brusa, M., Magnelli, B., Salvato, M., Mignoli, M., Zamorani, G., Fiore, F., Rosario, D. J., Mainieri, V., et al. (2012) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2012.22089.x · Cited by 253 · see citing works

Star-forming galaxies at very high redshifts

Lanzetta, K. M., Yahil, A., & Fernández-Soto, A. (1996) · Nature · doi:10.1038/381759a0 · Cited by 212 · see citing works

ANNz2: Photometric Redshift and Probability Distribution Function Estimation using Machine Learning

Sadeh, I., Abdalla, F. B., & Lahav, O. (2016) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/128/968/104502 · Cited by 202 · see citing works

On the physical properties ofz≈6–8 galaxies

Schaerer, D., & de Barros, S. (2010) · Astronomy and Astrophysics · doi:10.1051/0004-6361/200913946 · Cited by 198 · see citing works

A Critical Assessment Of Photometric Redshift Methods: A CANDELS Investigation

Wilson, G. (2016) · Texas ScholarWorks (Texas Digital Library) · doi:10.15781/t2kz5w · Cited by 194 · see citing works

J-PAS: The Javalambre-Physics of the Accelerated Universe Astrophysical SurveyPreprint

Benítez, N., Dupke, R. A., Moles, M., Junior, L. S., Cenarro, J., Marín-Franch, A., Taylor, K., Cristóbal, D., Fernández-Soto, A., de Oliveira, C. M., et al. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.5237 · Cited by 191 · see citing works

The WIRCam Deep Survey

Bielby, R. M., Hudelot, P., McCracken, H. J., Ilbert, O., Daddi, E., Fèvre, O. C. L., González-Pérez, V., Kneib, J., Marmo, C., Mellier, Y., et al. (2012) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201118547 · Cited by 189 · see citing works

Photometric redshifts for the CFHTLS T0004 deep and wide fields

Coupon, J., Ilbert, O., Kilbinger, M., McCracken, H. J., Mellier, Y., Arnouts, S., Bertin, E., Hudelot, P., Schultheis, M., Fèvre, O. C. L., et al. (2009) · Astronomy and Astrophysics · doi:10.1051/0004-6361/200811413 · Cited by 188 · see citing works

Photometric redshifts for the SDSS Data Release 12

Beck, R. E., Dobos, L., Budavári, T., Szalay, A. S., & Csabai, I. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stw1009 · Cited by 188 · see citing works

TFIT: A Photometry Package Using Prior Information for Mixed‐Resolution Data Sets

Laidler, V. G., Papovich, C., Grogin, N. A., Idzi, R., Dickinson, M., Ferguson, H. C., Hilbert, B., Clubb, K. I., & Ravindranath, S. (2007) · Publications of the Astronomical Society of the Pacific · doi:10.1086/523898 · Cited by 185 · see citing works

Photometric redshift analysis in the Dark Energy Survey Science Verification data

Sánchez, C., Kind, M. C., Lin, H., Miquel, R., Abdalla, F. B., Amara, A., Banerji, M., Bonnett, C., Brunner, R. J., Capozzi, D., et al. (2014) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stu1836 · Cited by 178 · see citing works

RANDOM FORESTS FOR PHOTOMETRIC REDSHIFTS

Carliles, S., Budavári, T., Heinis, S., Priebe, C. E., & Szalay, A. S. (2010) · The Astrophysical Journal · doi:10.1088/0004-637x/712/1/511 · Cited by 171 · see citing works

Photometric Redshifts and Selection of High-Redshift Galaxies in the NTT and Hubble Deep Fields

Fontana, A., D’Odorico, S., Poli, F., Giallongo, E., Arnouts, S., Cristiani, S., Moorwood, A. F. M., & Saracco, P. (2000) · The Astronomical Journal · doi:10.1086/316803 · Cited by 165 · see citing works

Photometric redshift estimation via deep learning

D’Isanto, A., & Polsterer, K. L. (2017) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201731326 · Cited by 165 · see citing works

A critical analysis of the ultraviolet continuum slopes (β) of high-redshift galaxies: no evidence (yet) for extreme stellar populations at z > 6

Dunlop, J. S., McLure, R. J., Robertson, B. E., Ellis, R. S., Stark, D. P., Cirasuolo, M., & de Ravel, L. (2011) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2011.20102.x · Cited by 160 · see citing works

T-PHOT: A new code for PSF-matched, prior-based, multiwavelength extragalactic deconfusion photometry

Merlin, E., Fontana, A., Ferguson, H. C., Dunlop, J. S., Elbaz, D., Bourne, N., Bruce, V. A., Buitrago, F., Castellano, M., Schreiber, C., et al. (2015) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201526471 · Cited by 159 · see citing works

The galaxy–halo connection from a joint lensing, clustering and abundance analysis in the CFHTLenS/VIPERS field

Coupon, J., Arnouts, S., Waerbeke, L. V., Moutard, T., Ilbert, O., van Uitert, E., Erben, T., Garilli, B., Guzzo, L., Heymans, C., et al. (2015) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv276 · Cited by 157 · see citing works

PHOTOMETRIC REDSHIFT WITH BAYESIAN PRIORS ON PHYSICAL PROPERTIES OF GALAXIES

Tanaka, M. (2015) · The Astrophysical Journal · doi:10.1088/0004-637x/801/1/20 · Cited by 154 · see citing works

IDENTIFICATIONS AND PHOTOMETRIC REDSHIFTS OF THE 2 Ms CHANDRA DEEP FIELD-SOUTH SOURCES

Luo, B., Brandt, W. N., Xue, Y. Q., Brusa, M., Alexander, D. M., Bauer, F. E., Comastri, A., Koekemoer, A. M., Lehmer, B. D., Mainieri, V., et al. (2010) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/187/2/560 · Cited by 153 · see citing works

The dominant role of mergers in the size evolution of massive early-type galaxies since z ~ 1

López-Sanjuán, C., Fèvre, O. C. L., Ilbert, O., Tasca, L. A. M., Bridge, C., Cucciati, O., Kampczyk, P., Pozzetti, L., Xu, C. K., Carollo, C. M., et al. (2012) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201219085 · Cited by 151 · see citing works

CANDELS/GOODS-S, CDFS, AND ECDFS: PHOTOMETRIC REDSHIFTS FOR NORMAL AND X-RAY-DETECTED GALAXIES

Hsu, L.-T., Salvato, M., Nandra, K., Brusa, M., Bender, R., Büchner, J., Donley, J. L., Kocevski, D. D., Guo, Y., Hathi, N. P., et al. (2014) · The Astrophysical Journal · doi:10.1088/0004-637x/796/1/60 · Cited by 150 · see citing works

Measuring photometric redshifts using galaxy images and Deep Neural Networks

Hoyle, B. (2016) · Astronomy and Computing · doi:10.1016/j.ascom.2016.03.006 · Cited by 144 · see citing works

SHARDS: AN OPTICAL SPECTRO-PHOTOMETRIC SURVEY OF DISTANT GALAXIES

Pérez‐González, P. G., Cava, A., Barro, G., Villar, V., Cardiel, N., Ferreras, I., Rodríguez-Espinosa, J. M., Alonso‐Herrero, A., Balcells, M., Cenarro, A. J., et al. (2012) · The Astrophysical Journal · doi:10.1088/0004-637x/762/1/46 · Cited by 136 · see citing works

PHOTOMETRIC REDSHIFTS AND QUASAR PROBABILITIES FROM A SINGLE, DATA-DRIVEN GENERATIVE MODEL

Bovy, J., Myers, A. D., Hennawi, J. F., Hogg, D. W., McMahon, R. G., Schiminovich, D., Sheldon, E. S., Brinkmann, J., Schneider, D. P., & Weaver, B. A. (2012) · The Astrophysical Journal · doi:10.1088/0004-637x/749/1/41 · Cited by 133 · see citing works

Relative merits of different types of rest-frame optical observations to constrain galaxy physical parameters

Pacifici, C., Charlot, S., Blaizot, J., & Brinchmann, J. (2012) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2012.20431.x · Cited by 132 · see citing works

The third data release of the Kilo-Degree Survey and associated data products

de Jong, J. D., Kleijn, G. V. V., Erben, T., Hildebrandt, H., Kuijken, K., Sikkema, G., Brescia, M., Bilicki, M., Napolitano, N., Amaro, V., et al. (2017) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201730747 · Cited by 132 · see citing works

RESOLVING THE DISCREPANCY OF GALAXY MERGER FRACTION MEASUREMENTS AT z ∼ 0–3

Man, A. W. S., Zirm, A., & Toft, S. (2016) · The Astrophysical Journal · doi:10.3847/0004-637x/830/2/89 · Cited by 128 · see citing works

Photometric redshifts with the Multilayer Perceptron Neural Network: Application to the HDF-S and SDSS

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SOMz: photometric redshift PDFs with self-organizing maps and random atlas

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Euclid preparation

Desprez, G., Paltani, S., Coupon, J., Almosallam, I. A., Ayllón, A. Á., Amaro, V., Brescia, M., Brodwin, M., Cavuoti, S., Vicente-Albendea, J. D., et al. (2020) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202039403 · Cited by 113 · see citing works

Photometric Redshifts for Next-Generation Surveys

Newman, J. A., & Gruen, D. (2022) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev-astro-032122-014611 · Cited by 112 · see citing works

All-purpose, all-sky photometric redshifts for the Legacy Imaging Surveys Data Release 8

Duncan, K. J. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac608 · Cited by 97 · see citing works

The LSST DESC DC2 Simulated Sky Survey

Abolfathi, B., Alonso, D., Armstrong, R., Aubourg, É., Awan, H., Babuji, Y. N., Bauer, F. E., Bean, R., Beckett, G., Biswas, R., et al. (2021) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/abd62c · Cited by 85 · see citing works

Self-supervised Representation Learning for Astronomical Images

Hayat, M. A., Stein, G., Harrington, P., Lukić, Z., & Mustafa, M. (2021) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/abf2c7 · Cited by 79 · see citing works

MAGPHYS+photo-z: Constraining the Physical Properties of Galaxies with Unknown Redshifts

Battisti, A. J., da Cunha, E., Grasha, K., Salvato, M., Daddi, E., Davies, L. J. M., Jin, S., Liu, D., Schinnerer, E., & Vaccari, M. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab345d · Cited by 78 · see citing works

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

Laigle, C., Davidzon, I., Ilbert, O., Devriendt, J., Kashino, D., Pichon, C., Capak, P. L., Arnouts, S., de la Torre, S., Dubois, Y., et al. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz1054 · Cited by 74 · see citing works

Modelling type 1 quasar colours in the era of Rubin and Euclid

Temple, M. J., Hewett, P. C., & Banerji, M. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2586 · Cited by 72 · see citing works

Inferring More from Less: Prospector as a Photometric Redshift Engine in the Era of JWST

Wang, B., Leja, J., Bezanson, R., Johnson, B. D., Khullar, G., Labbé, I., Price, S. H., Weaver, J. R., & Whitaker, K. E. (2023) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/acba99 · Cited by 69 · see citing works

Testing KiDS cross-correlation redshifts with simulations

van den Busch, J. L., Hildebrandt, H., Wright, A. H., Morrison, C. B., Blake, C., Joachimi, B., Erben, T., Heymans, C., Kuijken, K. H., & Taylor, E. N. (2020) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202038835 · Cited by 62 · see citing works

The galaxy cluster mass scale and its impact on cosmological constraints from the cluster population

Pratt, G. W., Arnaud, M., Biviano, A., Eckert, D., Ettori, S., Nagai, D., Okabe, N., & Reiprich, T. H. (2019) · MPG.PuRe (Max Planck Society) · Cited by 61 · see citing works

The PAU Survey: an improved photo-zsample in the COSMOS field

Alarcon, A., GAZTAÑAGA, E., Eriksen, M., Baugh, C. M., Cabayol, L., Casas, R., Carretero, J., Castander, F. J., de Vicente, J., Fernandez, E., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa3659 · Cited by 61 · see citing works

MAGPHYS+photo-z: Constraining the Physical Properties of Galaxies with Unknown Redshifts

Battisti, A. J., da Cunha, E., Grasha, K., Salvato, M., Daddi, E., Davies, L. J. M., Jin, S., Liu, D., Schinnerer, E., & Vaccari, M. (2019) · MPG.PuRe (Max Planck Society) · Cited by 61 · see citing works

Photometry of high-redshift blended galaxies using deep learning

Boucaud, A., Huertas-Company, M., Heneka, C., Ishida, E. E. O., Sedaghat, N., de Souza, R. S., Moews, B., Dole, H., Castellano, M., Merlin, E., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz3056 · Cited by 60 · see citing works

Dark Energy Survey year 1 results: Joint analysis of galaxy clustering, galaxy lensing, and CMB lensing two-point functions

Abbott, T. M. C., Abdalla, F. B., Alarcon, A., Allam, S., Annis, J. T., Avila, S., Aylor, K., Banerji, M., Banik, N., Baxter, E. J., et al. (2019) · Physical review. D/Physical review. D. · doi:10.1103/physrevd.100.023541 · Cited by 59 · see citing works

A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest

Mucesh, S., Hartley, W. G., Palmese, A., Lahav, O., Whiteway, L., Bluck, A. F. L., Alarcon, A., Amon, A., Bechtol, K., Bernstein, G. M., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab164 · Cited by 49 · see citing works

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

Kodra, D., Andrews, B. H., Newman, J. A., Finkelstein, S. L., Fontana, A., Hathi, N. P., Salvato, M., Wiklind, T., Wuyts, S., Broussard, A., et al. (2023) · The Astrophysical Journal · doi:10.3847/1538-4357/ac9f12 · Cited by 49 · see citing works

The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae

Aleo, P., Malanchev, K. L., Sharief, S. N., Jones, D. O., Narayan, G., Foley, R. J., Villar, V. A., Angus, C. R., Baldassare, V., Bustamante-Rosell, M. J., et al. (2023) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/acbfba · Cited by 48 · see citing works

Spectral Energy Distributions in Three Deep-drilling Fields of the Vera C. Rubin Observatory Legacy Survey of Space and Time: Source Classification and Galaxy Properties

Zou, F., Brandt, W. N., Chen, C., Leja, J., Ni, Q., Yan, W., Yang, G., Zhu, S., Luo, B., Nyland, K., et al. (2022) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac7bdf · Cited by 47 · see citing works

Consistent cosmic shear in the face of systematics: a B -mode analysis of KiDS-450, DES-SV and CFHTLenS

Asgari, M., Heymans, C., Hildebrandt, H., Miller, L., Schneider, P., Amon, A., Choi, A., Erben, T., Georgiou, C., Harnois-Déraps, J., et al. (2019) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201834379 · Cited by 47 · see citing works

Testing KiDS cross-correlation redshifts with simulations

van den Busch, J. L., Hildebrandt, H., Wright, A. H., Morrison, C., Blake, C., Joachimi, B., Erben, T., Heymans, C., Kuijken, K. H., & Taylor, E. N. (2020) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361/202038835/pdf · Cited by 47 · see citing works

A review of unsupervised learning in astronomy

Fotopoulou, S. (2024) · Astronomy and Computing · doi:10.1016/j.ascom.2024.100851 · Cited by 45 · see citing works

horizon-AGN virtual observatory – 2. Template-free estimates of galaxy properties from colours

Davidzon, I., Laigle, C., Capak, P., Ilbert, O., Masters, D., Hemmati, S., Apostolakos, N., Coupon, J., de la Torre, S., Devriendt, J., et al. (2019) · Monthly notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2486 · Cited by 25 · see citing works

Weak lensing tomographic redshift distribution inference for the Hyper Suprime-Cam Subaru Strategic Program three-year shape catalogue

Rau, M. M., Dalal, R., Zhang, T., Li, X., Nishizawa, A. J., More, S., Mandelbaum, R., Miyatake, H., Strauss, M. A., & Takada, M. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad1962 · Cited by 40 · see citing works

Photometric redshift-aided classification using ensemble learning

Cunha, P. A. C., & Humphrey, A. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202243135 · Cited by 40 · see citing works

The spectral energy distributions of active galactic nuclei

Brown, M. J. I., Duncan, K. J., Landt, H., Kirk, M., Ricci, C., Kamraj, N., Salvato, M., & Ananna, T. T. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2324 · Cited by 39 · see citing works

The PAU Survey: Photometric redshifts using transfer learning from simulations

Eriksen, M., Alarcon, A., Cabayol, L., Carretero, J., Casas, R., Castander, F. J., De Vicente, J., Fernandez, E., Garcia-Bellido, J., Gaztanaga, E., et al. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa2265 · Cited by 39 · see citing works

Photometric redshifts from SDSS images with an interpretable deep capsule network

Dey, B., Andrews, B. H., Newman, J. A., Mao, Y.-Y., Rau, M. M., & Zhou, R. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac2105 · Cited by 36 · see citing works

Narrow-line AGN selection in CEERS: Spectroscopic selection, physical properties, and X-ray and radio analysis

Mazzolari, G., Scholtz, J., Maiolino, R., Gilli, R., Traina, A., López, I. E., Übler, H., Trefoloni, B., D’Eugenio, F., Ji, X., et al. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202451860 · Cited by 34 · see citing works

The PANORAMIC Survey: Pure Parallel Wide Area Legacy Imaging with JWST/NIRCam

Williams, C. C., Oesch, P. A., Weibel, A., Brammer, G., Cloonan, A. P., Whitaker, K. E., Barrufet, L., Bezanson, R., Bowler, R. A. A., Dayal, P., et al. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ad97bc · Cited by 34 · see citing works

The miniJPAS survey: Photometric redshift catalogue

Hernán-Caballero, A., Varela, J., López-Sanjuan, C., Muniesa, D., Civera, T., Chaves-Montero, J., Díaz-García, L. A., Laur, J., Hernández-Monteagudo, C., Abramo, R., et al. (2021) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202141236 · Cited by 34 · see citing works

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

Graham, M. L., Connolly, A. J., Wang, W., Schmidt, S. J., Morrison, C. B., Ivezić, Ž., Fabbro, S., Côté, P., Daniel, S. F., Jones, L., et al. (2020) · The Astronomical Journal · doi:10.3847/1538-3881/ab8a43 · Cited by 32 · see citing works

Prospects of testing late-time cosmology with weak lensing of gravitational waves and galaxy surveys

Balaudo, A., Garoffolo, A., Martinelli, M., Mukherjee, S., & Silvestri, A. (2023) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2023/06/050 · Cited by 32 · see citing works

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 31 · see citing works

COSMOS Spectroscopic Redshift Compilation (First Data Release): 488,000 Redshifts Encompassing Two Decades of Spectroscopy

Khostovan, A. A., Kartaltepe, J. S., Salvato, M., Ilbert, O., Casey, C. M., Algera, H., Antwi-Danso, J., Battisti, A. J., Brinch, M., Brusa, M., et al. (2025) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ae1cb9 · Cited by 30 · see citing works

Improving the reliability of photometric redshift with machine learning

Razim, O., Cavuoti, S., Brescia, M., Riccio, G., Salvato, M., & Longo, G. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab2334 · Cited by 30 · see citing works

HYMALAIA: a hybrid lagrangian model for intrinsic alignments

Maion, F., Angulo, R. E., Bakx, T., Chisari, N. E., Kurita, T., & Pellejero-Ibáñez, M. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1331 · Cited by 30 · see citing works

COSMOS2020: Manifold learning to estimate physical parameters in large galaxy surveys

Davidzon, I., Jegatheesan, K., Ilbert, O., de la Torre, S., Leslie, S. K., Laigle, C., Hemmati, S., Masters, D. C., Blánquez-Sesé, D., Kauffmann, O. B., et al. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202243249 · Cited by 29 · see citing works

The cosmic web of X-ray active galactic nuclei seen through the eROSITA Final Equatorial Depth Survey (eFEDS)

Comparat, J., Luo, W., Merloni, A., More, S., Salvato, M., Krumpe, M., Miyaji, T., Brandt, W. N., Georgakakis, A. E., Akiyama, M., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202245726 · Cited by 29 · see citing works

X-Ray Redshifts for Obscured AGN: A Case Study in the J1030 Deep Field

Peca, A., Vignali, C., Gilli, R., Mignoli, M., Nanni, R., Marchesi, S., Bolzonella, M., Brusa, M., Cappelluti, N., Comastri, A., et al. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/abc9c7 · Cited by 26 · see citing works

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

Brescia, M., Salvato, M., Cavuoti, S., Ananna, T. T., Riccio, G., LaMassa, S. M., Urry, C. M., & Longo, G. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz2159 · Cited by 26 · see citing works

Euclid preparation

Ilbert, O., de la Torre, S., Martinet, N., Wright, A. H., Paltani, S., Laigle, C., Davidzon, I., Jullo, E., Hildebrandt, H., Masters, D. C., et al. (2021) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202040237 · Cited by 25 · see citing works

Potential scientific synergies in weak lensing studies between the CSST andEuclidspace probes

Liu, D., Meng, X. M., Er, X., Fan, Z., Kilbinger, M., Li, G. L., Li, R., Schrabback, T., Scognamiglio, D., Shan, H., et al. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202243978 · Cited by 25 · see citing works

Foreword to the Focus Issue on Machine Intelligence in Astronomy and Astrophysics

Longo, G., Merényi, E., & Tiňo, P. (2019) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/ab2743 · Cited by 24 · see citing works

Twenty-First-Century Statistical and Computational Challenges in Astrophysics

Feigelson, E. D., de Souza, R. S., Ishida, É. E. O., & Babu, G. J. (2021) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-042720-112045 · Cited by 24 · see citing works

A comparison of photometric redshift techniques for large radio surveys

Norris, R. P., Salvato, M., Longo, G., Brescia, M., Budavári, T., Carliles, S., Cavuoti, S., Farrah, D., Geach, J. E., Luken, K., et al. (2019) · MPG.PuRe (Max Planck Society) · Cited by 23 · see citing works

Genuine Retrieval of the AGN Host Stellar Population (GRAHSP)

Büchner, J., Starck, H., Salvato, M., Netzer, H., Igo, Z., Laloux, B., Georgakakis, A. E., Gauger, I., Olechowska, A., Lopez, N., et al. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202449372 · Cited by 22 · see citing works

Cosmological constraints from weak lensing scattering transform using HSC Y1 data

Cheng, S.-H., Marques, G. A., Grand'on, D., Thiele, L., Shirasaki, M., M'enard, B., & Liu, J. (2024) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2025/01/006 · Cited by 20 · see citing works

Augmenting machine learning photometric redshifts with Gaussian mixture models

Hatfield, P., Almosallam, I. A., Jarvis, M. J., Adams, N., Bowler, R. A. A., Gomes, Z., Roberts, S., & Schreiber, C. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa2741 · Cited by 22 · see citing works

Self-calibration and robust propagation of photometric redshift distribution uncertainties in weak gravitational lensing

Stölzner, B., Joachimi, B., Korn, A., Hildebrandt, H., & Wright, A. H. (2021) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202040130 · Cited by 22 · see citing works

QSO photometric redshifts using machine learning and neural networks

Curran, S. J., Moss, J., & Perrott, Y. C. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab485 · Cited by 22 · see citing works

Redshift requirements for cosmic shear with intrinsic alignment

Fischbacher, S., Kacprzak, T., Blazek, J., & Réfrégier, A. (2022) · Journal of Cosmology and Astroparticle Physics · doi:10.1088/1475-7516/2023/01/033 · Cited by 14 · see citing works

A Comparison of Photometric Redshift Techniques for Large Radio Surveys

Norris, R. P., Salvato, M., Longo, G., Brescia, M., Budavari, T., Carliles, S., Cavuoti, S., Farrah, D., Geach, J., Luken, K., et al. (2019) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/ab0f7b · Cited by 22 · see citing works

The PAU Survey: narrow-band photometric redshifts using Gaussian processes

Soo, J. Y. H., Joachimi, B., Eriksen, M., Siudek, M., Alarcon, A., Cabayol, L., Carretero, J., Casas, R., Castander, F. J., Fernandez, E., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab711 · Cited by 22 · see citing works

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

Simet, M., Chartab, N., Lu, Y., & Mobasher, B. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/abd179 · Cited by 21 · see citing works

The PAU Survey: Intrinsic alignments and clustering of narrow-band photometric galaxies

Johnston, H., Joachimi, B., Norberg, P., Hoekstra, H., Eriksen, M., Fortuna, M. C., Manzoni, G., Serrano, S., Siudek, M., Tortorelli, L., et al. (2021) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202039682 · Cited by 21 · see citing works

Hybrid photometric redshifts for sources in the COSMOS and XMM-LSS fields

Hatfield, P., Jarvis, M. J., Adams, N., Bowler, R. A. A., Häußler, B., & Duncan, K. J. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac1042 · Cited by 20 · see citing works

Photometric redshift uncertainties in weak gravitational lensing shear analysis: models and marginalization

Zhang, T., Rau, M. M., Mandelbaum, R., Li, X., & Moews, B. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3090 · Cited by 20 · see citing works

Preliminary Results of Using k-nearest-neighbor Regression to Estimate the Redshift of Radio-selected Data Sets

Luken, K., Norris, R. P., & Park, L. A. F. (2019) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/aaea17 · Cited by 20 · see citing works

The PAU Survey: Intrinsic alignments and clustering of narrow-band photometric galaxies

Johnston, H., Joachimi, B., Norberg, P., Hoekstra, H., Eriksen, M., Fortuna, M. C., Manzoni, G., Serrano, S., Siudek, M., Tortorelli, L., et al. (2021) · Springer Link (Chiba Institute of Technology) · doi:10.1051/0004-6361/202039682/pdf · Cited by 19 · see citing works

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

Hong, S., Zou, Z., Luo, A.-L., Kong, X., Yang, W., & Chen, Y. (2022) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stac3259 · Cited by 19 · see citing works

Photometric redshift estimation for CSST survey with LSTM neural networks

Luo, Z., Li, Y., Lu, J., Chen, Z., Fu, L., Zhang, S., Xiao, H., Du, W., Gong, Y., Shu, C., et al. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae2446 · Cited by 18 · see citing works

Using angular two-point correlations to self-calibrate the photometric redshift distributions of DECaLS DR9

Xu, H., Zhang, P., Peng, H. F., Yu, Y., Zhang, L., Yao, J., Qin, J., Sun, Z., He, M., & Yang, X. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad136 · Cited by 18 · see citing works

A composite likelihood approach for inference under photometric redshift uncertainty

Rau, M. M., Morrison, C., Schmidt, S. J., Wilson, S., Mandelbaum, R., Mao, Y.-Y., Alonso, D., Hartley, W. G., Kirkby, D. P., Kuusela, M., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab3290 · Cited by 18 · see citing works

Calibrating Photometric Redshift Measurements with the Multi-channel Imager (MCI) of the China Space Station Telescope (CSST)

Cao, Y., Gong, Y., Zheng, Z.-Y., & Xu, C. (2021) · Research in Astronomy and Astrophysics · doi:10.1088/1674-4527/ac424e · Cited by 18 · see citing works

TOPz: Photometric redshifts for J-PAS

Laur, J., Tempel, E., Tamm, A., Kipper, R., Liivamägi, L. J., Hernán-Caballero, A., Muru, M. M., Chaves-Montero, J., Díaz-García, L. A., Turner, S., et al. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202243881 · Cited by 17 · see citing works

CIRCLEZ : Reliable photometric redshifts for active galactic nuclei computed solely using photometry from Legacy Survey Imaging for DESI

Saxena, A., Salvato, M., Roster, W., Shirley, R. A., Büchner, J., Wolf, J., Kohl, C., Starck, H., Dwelly, T., Comparat, J., et al. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202450886 · Cited by 17 · see citing works

QSO photometric redshifts from SDSS, WISE, and GALEX colours

Curran, S. J. (2020) · Monthly Notices of the Royal Astronomical Society Letters · doi:10.1093/mnrasl/slaa012 · Cited by 17 · see citing works

Counterpart identification and classification for eRASS1 and characterisation of the active galactic nuclei content

Salvato, M., Wolf, J., Dwelly, T., Starck, H., Büchner, J., Shirley, R. A., Merloni, A., Georgakakis, A. E., Balzer, F., Brusa, M., et al. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202556142 · Cited by 16 · see citing works

Photometric redshift estimation with convolutional neural networks and galaxy images: Case study of resolving biases in data-driven methods

Lin, Q., Fouchez, D., Pasquet, J., Treyer, M. A., Ouahmed, R. A., Arnouts, S., & Ilbert, O. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202142751 · Cited by 16 · see citing works

Photometric redshifts for the Pan-STARRS1 survey

Tarrío, P., & Zarattini, S. (2020) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202038415 · Cited by 16 · see citing works

Estimating photometric redshift from mock flux for CSST survey by using weighted Random Forest

Lu, J., Luo, Z., Chen, Z., Fu, L., Du, W., Gong, Y., Li, Y., Meng, X., Tang, Z., Zhang, S., et al. (2023) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stad3976 · Cited by 15 · see citing works

Imputation of missing photometric data and photometric redshift estimation for CSST

Luo, Z., Tang, Z., Chen, Z., Fu, L., Du, W., Zhang, S., Gong, Y., Shu, C., Lu, J., Li, Y., et al. (2024) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stae1397 · Cited by 15 · see citing works

Exploring galactic properties with machine learning

Zeraatgari, F. Z., Hafezianzadeh, F., Zhang, Y., Mosallanezhad, A., & Zhang, J. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202348714 · Cited by 15 · see citing works

Stellar Mass Calibrations for Local Low-mass Galaxies

Reyes, M. A. C. D. L., Asali, Y., Wechsler, R. H., Geha, M., Mao, Y.-Y., Kado-Fong, E., Pucha, R., Grant, W., Gandhi, P. J., Manwadkar, V., et al. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/ade4c5 · Cited by 15 · see citing works

Mixture models for photometric redshifts

Ansari, Z., Agnello, A., & Gall, C. (2021) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202039675 · Cited by 15 · see citing works

Identifying AGN Host Galaxies by Machine Learning with HSC+WISE

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PhotoWeb redshift: boosting photometric redshift accuracy with large spectroscopic surveys

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