Photometric redshifts from SDSS images using a convolutional neural network
A&A, 621 (2019) A26
Published online: 2018-12-21
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Lin Yao, Bo Qiu, A-Li Luo, Jianwei Zhou, Kuang Wu, Xiao Kong, Yuanbo Liu, Guiyu Zhao and Kun WangMonthly Notices of the Royal Astronomical Society 523 (4) 5799 (2023)
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Searching for Dwarf Hα Emission-line Galaxies within Voids. I. Survey Methods and First Observations
Christian D. Draper, J. Ward Moody, Stephen R. McNeil, Michael D. Joner, Rochelle Steele and Jackson SteeleThe Astrophysical Journal 950 (2) 189 (2023)
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Euclid preparation
A. Humphrey, L. Bisigello, P. A. C. Cunha, et al.Astronomy & Astrophysics 671 A99 (2023)
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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)
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The PAU Survey and Euclid: Improving broadband photometric redshifts with multi-task learning
L. Cabayol, M. Eriksen, J. Carretero, et al.Astronomy & Astrophysics 671 A153 (2023)
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Augmenting photometric redshift estimates using spectroscopic nearest neighbours
F. Tosone, M. S. Cagliari, L. Guzzo, B. R. Granett and A. CrespiAstronomy & Astrophysics 672 A150 (2023)
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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)
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The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys
M. Huertas-Company and F. LanussePublications of the Astronomical Society of Australia 40 (2023)
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Supernova search with active learning in ZTF DR3
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Wasserstein distance as a new tool for discriminating cosmologies through the topology of large-scale structure
Maksym Tsizh, Vitalii Tymchyshyn and Franco VazzaMonthly Notices of the Royal Astronomical Society 522 (2) 2697 (2023)
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Identification of Galaxy–Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
E. A. Zaborowski, A. Drlica-Wagner, F. Ashmead, J. F. Wu, R. Morgan, C. R. Bom, A. J. Shajib, S. Birrer, W. Cerny, E. J. Buckley-Geer, B. Mutlu-Pakdil, P. S. Ferguson, K. Glazebrook, S. J. Gonzalez Lozano, Y. Gordon, M. Martinez, V. Manwadkar, J. O’Donnell, J. Poh, A. Riley, J. D. Sakowska, L. Santana-Silva, B. X. Santiago, D. Sluse, C. Y. Tan, et al.The Astrophysical Journal 954 (1) 68 (2023)
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Combining the CLAUDS and HSC-SSP surveys
G. Desprez, V. Picouet, T. Moutard, et al.Astronomy & Astrophysics 670 A82 (2023)
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The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae
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Machine learning for observational cosmology
Kana Moriwaki, Takahiro Nishimichi and Naoki YoshidaReports on Progress in Physics 86 (7) 076901 (2023)
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Preliminary Study of Photometric Redshifts Based on the Wide Field Survey Telescope
Yu Liu, Xiao-Zhi Lin, Yong-Quan Xue and Huynh Anh N. LeResearch in Astronomy and Astrophysics 23 (12) 125011 (2023)
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Data-driven templates with dictionary learning and sparse representations for photometric redshift estimation
J. Frontera-Pons, F. Sureau, J. Bobin and M. KilbingerAstronomy and Computing 44 100735 (2023)
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The miniJPAS survey quasar selection – II. Machine learning classification with photometric measurements and uncertainties
Natália V N Rodrigues, L Raul Abramo, Carolina Queiroz, Ginés Martínez-Solaeche, Ignasi Pérez-Ràfols, Silvia Bonoli, Jonás Chaves-Montero, Matthew M Pieri, Rosa M González Delgado, Sean S Morrison, Valerio Marra, Isabel Márquez, A Hernán-Caballero, L A Díaz-García, Narciso Benítez, A Javier Cenarro, Renato A Dupke, Alessandro Ederoclite, Carlos López-Sanjuan, Antonio Marín-Franch, Claudia Mendes de Oliveira, Mariano Moles, Laerte Sodré, Jesús Varela, Héctor Vázquez Ramió and Keith TaylorMonthly Notices of the Royal Astronomical Society 520 (3) 3494 (2023)
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High-fidelity reproduction of central galaxy joint distributions with neural networks
Natália V N Rodrigues, Natalí S M de Santi, Antonio D Montero-Dorta and L Raul AbramoMonthly Notices of the Royal Astronomical Society 522 (3) 3236 (2023)
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Photometric redshift estimation with convolutional neural networks and galaxy images: Case study of resolving biases in data-driven methods
Q. Lin, D. Fouchez, J. Pasquet, et al.Astronomy & Astrophysics 662 A36 (2022)
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Machine learning technique for morphological classification of galaxies from the SDSS. III. The CNN image-based inference of detailed features
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Machine learning in astronomy
Ajit Kembhavi and Rohan PattnaikJournal of Astrophysics and Astronomy 43 (2) (2022)
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Photometric Redshifts for Next-Generation Surveys
Jeffrey A. Newman and Daniel GruenAnnual Review of Astronomy and Astrophysics 60 (1) 363 (2022)
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Photometric identification of compact galaxies, stars, and quasars using multiple neural networks
Siddharth Chaini, Atharva Bagul, Anish Deshpande, Rishi Gondkar, Kaushal Sharma, M Vivek and Ajit KembhaviMonthly Notices of the Royal Astronomical Society 518 (2) 3123 (2022)
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Extracting photometric redshift from galaxy flux and image data using neural networks in the CSST survey
Xingchen Zhou, Yan Gong, Xian-Min Meng, et al.Monthly Notices of the Royal Astronomical Society 512 (3) 4593 (2022)
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