Photometric redshift estimation via deep learning - Generalized and pre-classification-less, image based, fully probabilistic redshifts
A&A, 609 (2018) A111
Published online: 2018-01-25
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:
This article has been cited by the following article(s):
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. NakonecznyAstronomy & Astrophysics 698 A276 (2025)
https://doi.org/10.1051/0004-6361/202453576
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 FanResearch in Astronomy and Astrophysics 25 (5) 055021 (2025)
https://doi.org/10.1088/1674-4527/adcc7e
Parameter measurement based on photometric images
Jiang-Hui Cai, Na Li, Hai-Feng Yang, Li-Li Wang, Ai-Yu Zheng, Jing Hao, Xujun Zhao and Yaling XunAstronomy & Astrophysics 694 A271 (2025)
https://doi.org/10.1051/0004-6361/202452813
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 evaluation of efficient interpretable CNN-transformer model for redshift prediction
Snigdha Sen, Ambuj Kumar Pandit, Pavan Chakraborty and Krishna Pratap SinghSignal, Image and Video Processing 19 (4) (2025)
https://doi.org/10.1007/s11760-025-03855-9
Machine Learning–based Photometric Redshifts for Galaxies in the North Ecliptic Pole Wide Field: Catalogs of Spectroscopic and Photometric Redshifts
Taewan Kim, Jubee Sohn, Ho Seong Hwang, Simon C.-C. Ho, Denis Burgarella, Tomotsugu Goto, Tetsuya Hashimoto, Woong-Seob Jeong, Seong Jin Kim, Matthew A. Malkan, Takamitsu Miyaji, Nagisa Oi, Hyunjin Shim, Hyunmi Song, Narae Hwang and Byeong-Gon ParkThe Astrophysical Journal Supplement Series 277 (2) 41 (2025)
https://doi.org/10.3847/1538-4365/adb42a
Predicting the Physical Properties of Dark Matter Subhalos from Baryonic Parameters Using Machine Learning
Moonzarin RezaNew Astronomy 115 102316 (2025)
https://doi.org/10.1016/j.newast.2024.102316
Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution
Dilbag Singh, Ahmad Ali Alzubi, Manjit Kaur, Vijay Kumar and Heung-No LeeIEEE Journal of Biomedical and Health Informatics 29 (1) 669 (2025)
https://doi.org/10.1109/JBHI.2024.3377631
HOLISMOKES
S. Schuldt, R. Cañameras, I. T. Andika, S. Bag, A. Melo, Y. Shu, S. H. Suyu, S. Taubenberger and C. GrilloAstronomy & Astrophysics 693 A291 (2025)
https://doi.org/10.1051/0004-6361/202450927
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 FanMonthly Notices of the Royal Astronomical Society 535 (2) 1844 (2024)
https://doi.org/10.1093/mnras/stae2446
HOLISMOKES
R. Cañameras, S. Schuldt, Y. Shu, S. H. Suyu, S. Taubenberger, I. T. Andika, S. Bag, K. T. Inoue, A. T. Jaelani, L. Leal-Taixé, T. Meinhardt, A. Melo and A. MoreAstronomy & Astrophysics 692 A72 (2024)
https://doi.org/10.1051/0004-6361/202347072
Galaxy Spectroscopy without Spectra: Galaxy Properties from Photometric Images with Conditional Diffusion Models
Lars Doorenbos, Eva Sextl, Kevin Heng, Stefano Cavuoti, Massimo Brescia, Olena Torbaniuk, Giuseppe Longo, Raphael Sznitman and Pablo Márquez-NeilaThe Astrophysical Journal 977 (1) 131 (2024)
https://doi.org/10.3847/1538-4357/ad8bbe
Mining for Protoclusters at z ∼ 4 from Photometric Data Sets with Deep Learning
Yoshihiro Takeda, Nobunari Kashikawa, Kei Ito, Jun Toshikawa, Rieko Momose, Kent Fujiwara, Yongming Liang, Rikako Ishimoto, Takehiro Yoshioka, Junya Arita, Mariko Kubo and Hisakazu UchiyamaThe Astrophysical Journal 977 (1) 81 (2024)
https://doi.org/10.3847/1538-4357/ad8a67
Decompositions of the mean continuous ranked probability score
Sebastian Arnold, Eva-Maria Walz, Johanna Ziegel and Tilmann GneitingElectronic Journal of Statistics 18 (2) (2024)
https://doi.org/10.1214/24-EJS2316
CLAP
Qiufan Lin, Hengxin Ruan, Dominique Fouchez, Shupei Chen, Rui Li, Paulo Montero-Camacho, Nicola R. Napolitano, Yuan-Sen Ting and Wei ZhangAstronomy & Astrophysics 691 A331 (2024)
https://doi.org/10.1051/0004-6361/202349113
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
Photometric Redshift Estimation of Quasars by a Cross-modal Contrast Learning Method
Chen Zhang, Wenyu Wang, Meixia Qu, Bin Jiang and YanXia ZhangThe Astronomical Journal 168 (6) 244 (2024)
https://doi.org/10.3847/1538-3881/ad79f9
A dark standard siren measurement of the Hubble constant following LIGO/Virgo/KAGRA O4a and previous runs
C R Bom, V Alfradique, A Palmese, G Teixeira, L Santana-Silva, A Santos and P DarcMonthly Notices of the Royal Astronomical Society 535 (1) 961 (2024)
https://doi.org/10.1093/mnras/stae2390
SRGz: классификация точечных рентгеновских источников еРОЗИТА в области 1%DESI и калибровка фотометрических красных смещений
А. В. Мещеряков, Г. А. Хорунжев, С. А. Воскресенская1, П. С. Медведев, М. Р. Гильфанов and Р. А. СюняевPisʹma v Astronomičeskij žurnal 50 (1) 38 (2024)
https://doi.org/10.31857/S0320010824010031
Testing the transferability of machine learning techniques for determining photometric redshifts of galaxy catalogue populations
Lara Janiurek, Martin A Hendry and Fiona C SpeiritsMonthly Notices of the Royal Astronomical Society 533 (3) 2786 (2024)
https://doi.org/10.1093/mnras/stae1901
Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2
G. Teixeira, C.R. Bom, L. Santana-Silva, B.M.O. Fraga, P. Darc, R. Teixeira, J.F. Wu, P.S. Ferguson, C.E. Martínez-Vázquez, A.H. Riley, A. Drlica-Wagner, Y. Choi, B. Mutlu-Pakdil, A.B. Pace, J.D. Sakowska and G.S. StringfellowAstronomy and Computing 49 100886 (2024)
https://doi.org/10.1016/j.ascom.2024.100886
1 (2024)
https://doi.org/10.1109/e-Science62913.2024.10678724
Exploring galactic properties with machine learning
F. Z. Zeraatgari, F. Hafezianzadeh, Y.-X. Zhang, A. Mosallanezhad and J.-Y. ZhangAstronomy & Astrophysics 688 A33 (2024)
https://doi.org/10.1051/0004-6361/202348714
Unsupervised Domain Adaptation for Constraining Star Formation Histories
Sankalp Gilda, Antoine de Mathelin, Sabine Bellstedt and Guillaume RichardAstronomy 3 (3) 189 (2024)
https://doi.org/10.3390/astronomy3030012
Galaxy Spectra neural Network (GaSNet). II. Using deep learning for spectral classification and redshift predictions
Fucheng Zhong, Nicola R Napolitano, Caroline Heneka, Rui Li, Franz Erik Bauer, Nicolas Bouche, Johan Comparat, Young-Lo Kim, Jens-Kristian Krogager, Marcella Longhetti, Jonathan Loveday, Boudewijn F Roukema, Benedict L Rouse, Mara Salvato, Crescenzo Tortora, Roberto J Assef, Letizia P Cassarà, Luca Costantin, Scott M Croom, Luke J M Davies, Alexander Fritz, Guillaume Guiglion, Andrew Humphrey, Emanuela Pompei, Claudio Ricci, et al.Monthly Notices of the Royal Astronomical Society 532 (1) 643 (2024)
https://doi.org/10.1093/mnras/stae1461
Stellar Classification with Vision Transformer and SDSS Photometric Images
Yi Yang and Xin LiUniverse 10 (5) 214 (2024)
https://doi.org/10.3390/universe10050214
hayate: photometric redshift estimation by hybridizing machine learning with template fitting
Shingo Tanigawa, K Glazebrook, C Jacobs, I Labbe and A K QinMonthly Notices of the Royal Astronomical Society 530 (2) 2012 (2024)
https://doi.org/10.1093/mnras/stae411
Easy Uncertainty Quantification (EasyUQ): Generating Predictive Distributions from Single-Valued Model Output
Eva-Maria Walz, Alexander Henzi, Johanna Ziegel and Tilmann GneitingSIAM Review 66 (1) 91 (2024)
https://doi.org/10.1137/22M1541915
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. NandraAstronomy & Astrophysics 692 A260 (2024)
https://doi.org/10.1051/0004-6361/202452361
Deep learning prediction of galaxy stellar populations in the low-redshift Universe
Li-Li Wang, Guang-Jun Yang, Jun-Liang Zhang, Li-Xia Rong, Wen-Yan Zheng, Cong Liu and Zong-Yi ChenMonthly Notices of the Royal Astronomical Society 527 (4) 10557 (2023)
https://doi.org/10.1093/mnras/stad3756
CNN photometric redshifts in the SDSS at r ≤ 20
M Treyer, R Ait Ouahmed, J Pasquet, S Arnouts, E Bertin and D FouchezMonthly Notices of the Royal Astronomical Society 527 (1) 651 (2023)
https://doi.org/10.1093/mnras/stad3171
A Concept of Assessment of LIV Tests with THESEUS Using the Gamma-Ray Bursts Detected by Fermi/GBM
Anastasia Tsvetkova, Luciano Burderi, Alessandro Riggio, Andrea Sanna and Tiziana Di SalvoUniverse 9 (8) 359 (2023)
https://doi.org/10.3390/universe9080359
Measuring photometric redshifts for high-redshift radio source surveys
K. J. Luken, R. P. Norris, X. R. Wang, L. A. F. Park, Y. Guo and M. D. FilipovićPublications of the Astronomical Society of Australia 40 (2023)
https://doi.org/10.1017/pasa.2023.39
CAvity DEtection Tool (CADET): pipeline for detection of X-ray cavities in hot galactic and cluster atmospheres
T Plšek, N Werner, M Topinka and A SimionescuMonthly Notices of the Royal Astronomical Society 527 (2) 3315 (2023)
https://doi.org/10.1093/mnras/stad3371
SRGz: Classification of eROSITA Point X-ray Sources in the 1$${\%}$$DESI Region and Calibration of Photometric Redshifts*
A. V. Meshcheryakov, G. A. Khorunzhev, S. A. Voskresenskaya, P. S. Medvedev, M. R. Gilfanov and R. A. SunyaevAstronomy Letters 49 (11) 646 (2023)
https://doi.org/10.1134/S1063773723110129
AutoSourceID-FeatureExtractor
F. Stoppa, R. Ruiz de Austri, P. Vreeswijk, S. Bhattacharyya, S. Caron, S. Bloemen, G. Zaharijas, G. Principe, V. Vodeb, P. J. Groot, E. Cator and G. NelemansAstronomy & Astrophysics 680 A108 (2023)
https://doi.org/10.1051/0004-6361/202346983
DPQP: A Detection Pipeline for Quasar Pair Candidates Based on QSO Photometric Images and Spectra
Yuanbo Liu, Bo Qiu, A-li Luo, Xia Jiang, Lin Yao, Kun Wang and Guiyu ZhaoUniverse 9 (9) 425 (2023)
https://doi.org/10.3390/universe9090425
Classifying MaNGA velocity dispersion profiles by machine learning
Yi Duann, Yong Tian and Chung-Ming KoRAS Techniques and Instruments 2 (1) 649 (2023)
https://doi.org/10.1093/rasti/rzad044
60 159 (2023)
https://doi.org/10.1007/978-3-031-34167-0_32
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)
https://doi.org/10.1017/pasa.2022.55
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 WangMonthly Notices of the Royal Astronomical Society 523 (4) 5799 (2023)
https://doi.org/10.1093/mnras/stad1842
Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning
Helen Qu and Masao SakoThe Astrophysical Journal 954 (2) 201 (2023)
https://doi.org/10.3847/1538-4357/aceafa
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)
https://doi.org/10.1051/0004-6361/202245027
Stellar classification with convolutional neural networks and photometric images: a new catalogue of 50 million SDSS stars without spectra
Jing-Hang Shi, Bo Qiu, A-Li Luo, et al.Monthly Notices of the Royal Astronomical Society 520 (2) 2269 (2023)
https://doi.org/10.1093/mnras/stad255
Detection of Einstein telescope gravitational wave signals from binary black holes using deep learning
Wathela Alhassan, T Bulik and M SuchenekMonthly Notices of the Royal Astronomical Society 519 (3) 3843 (2023)
https://doi.org/10.1093/mnras/stac3797
Dealing with imbalanced regression problem for large dataset using scalable Artificial Neural Network
Snigdha Sen, Krishna Pratap Singh and Pavan ChakrabortyNew Astronomy 99 101959 (2023)
https://doi.org/10.1016/j.newast.2022.101959
Combining the CLAUDS and HSC-SSP surveys
G. Desprez, V. Picouet, T. Moutard, et al.Astronomy & Astrophysics 670 A82 (2023)
https://doi.org/10.1051/0004-6361/202243363
Revised Extinctions and Radii for 1.5 Million Stars Observed by APOGEE, GALAH, and RAVE
Jie Yu, Shourya Khanna, Nathalie Themessl, Saskia Hekker, Guillaume Dréau, Laurent Gizon and Shaolan BiThe Astrophysical Journal Supplement Series 264 (2) 41 (2023)
https://doi.org/10.3847/1538-4365/acabc8
Galaxy morphoto-Z with neural Networks (GaZNets)
Rui Li, Nicola R. Napolitano, Haicheng Feng, et al.Astronomy & Astrophysics 666 A85 (2022)
https://doi.org/10.1051/0004-6361/202244081
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)
https://doi.org/10.1093/mnras/stac3336
Improving the accuracy of single-trial fMRI response estimates using GLMsingle
Jacob S Prince, Ian Charest, Jan W Kurzawski, et al.eLife 11 (2022)
https://doi.org/10.7554/eLife.77599
Machine learning in astronomy
Ajit Kembhavi and Rohan PattnaikJournal of Astrophysics and Astronomy 43 (2) (2022)
https://doi.org/10.1007/s12036-022-09871-2
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 RinaldiThe Astrophysical Journal Supplement Series 259 (2) 55 (2022)
https://doi.org/10.3847/1538-4365/ac545a
Evaluation of machine learning techniques for forecast uncertainty quantification
Maximiliano A. Sacco, Juan J. Ruiz, Manuel Pulido and Pierre TandeoQuarterly Journal of the Royal Meteorological Society 148 (749) 3470 (2022)
https://doi.org/10.1002/qj.4362
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
Spectroscopic redshift determination with Bayesian convolutional networks
O. Podsztavek, P. Škoda and P. TvrdíkAstronomy and Computing 40 100615 (2022)
https://doi.org/10.1016/j.ascom.2022.100615
Soft-labeling approach along with an ensemble of models for predicting subjective freshness of spinach leaves
Kento Koyama and Suxing LyuComputers and Electronics in Agriculture 193 106633 (2022)
https://doi.org/10.1016/j.compag.2021.106633
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)
https://doi.org/10.1051/0004-6361/202142751
ulisse: A tool for one-shot sky exploration and its application for detection of active galactic nuclei
Lars Doorenbos, Olena Torbaniuk, Stefano Cavuoti, et al.Astronomy & Astrophysics 666 A171 (2022)
https://doi.org/10.1051/0004-6361/202243900
Estimation of Photometric Redshifts. II. Identification of Out-of-distribution Data with Neural Networks
Joongoo Lee and Min-Su ShinThe Astronomical Journal 163 (2) 98 (2022)
https://doi.org/10.3847/1538-3881/ac4335
Deep learning methods for obtaining photometric redshift estimations from images
Ben Henghes, Jeyan Thiyagalingam, Connor Pettitt, Tony Hey and Ofer LahavMonthly Notices of the Royal Astronomical Society 512 (2) 1696 (2022)
https://doi.org/10.1093/mnras/stac480
Quasar photometric redshifts from incomplete data using deep learning
S J CurranMonthly Notices of the Royal Astronomical Society 512 (2) 2099 (2022)
https://doi.org/10.1093/mnras/stac660
SDSS-IV MaNGA: Unveiling Galaxy Interaction by Merger Stages with Machine Learning
Yu-Yen Chang, Lihwai Lin, Hsi-An Pan, Chieh-An Lin, Bau-Ching Hsieh, Connor Bottrell and Pin-Wei WangThe Astrophysical Journal 937 (2) 97 (2022)
https://doi.org/10.3847/1538-4357/ac8c27
Mimicking the halo–galaxy connection using machine learning
Natalí S M de Santi, Natália V N Rodrigues, Antonio D Montero-Dorta, et al.Monthly Notices of the Royal Astronomical Society 514 (2) 2463 (2022)
https://doi.org/10.1093/mnras/stac1469
Estimating galaxy redshift in radio-selected datasets using machine learning
K.J. Luken, R.P. Norris, L.A.F. Park, X.R. Wang and M.D. FilipovićAstronomy and Computing 39 100557 (2022)
https://doi.org/10.1016/j.ascom.2022.100557
PhotoRedshift-MML: A multimodal machine learning method for estimating photometric redshifts of quasars
Shuxin Hong, Zhiqiang Zou, A-Li Luo, et al.Monthly Notices of the Royal Astronomical Society 518 (4) 5049 (2022)
https://doi.org/10.1093/mnras/stac3259
Fuzzy and SVM Based Classification Model to Classify Spectral Objects in Sloan Digital Sky
Arodh Lal Karn, Carlos Andres Tavera Romero, Sudhakar Sengan, Abolfazl Mehbodniya, Julian L. Webber, Denis A. Pustokhin and Frank-Detlef WendeIEEE Access 10 101276 (2022)
https://doi.org/10.1109/ACCESS.2022.3207480
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 ZhouMonthly Notices of the Royal Astronomical Society 515 (4) 5285 (2022)
https://doi.org/10.1093/mnras/stac2105
Hubble Asteroid Hunter
Emily O. Garvin, Sandor Kruk, Claude Cornen, et al.Astronomy & Astrophysics 667 A141 (2022)
https://doi.org/10.1051/0004-6361/202243745
GeneticKNN: a weighted KNN approach supported by genetic algorithm for photometric redshift estimation of quasars
Bo Han, Li-Na Qiao, Jing-Lin Chen, et al.Research in Astronomy and Astrophysics 21 (1) 017 (2021)
https://doi.org/10.1088/1674-4527/21/1/17
Identifying AGN Host Galaxies by Machine Learning with HSC+WISE
Yu-Yen Chang, Bau-Ching Hsieh, Wei-Hao Wang, Yen-Ting Lin, Chen-Fatt Lim, Yoshiki Toba, Yuxing Zhong and Siou-Yu ChangThe Astrophysical Journal 920 (2) 68 (2021)
https://doi.org/10.3847/1538-4357/ac167c
Self-supervised Representation Learning for Astronomical Images
Md Abul Hayat, George Stein, Peter Harrington, Zarija Lukić and Mustafa MustafaThe Astrophysical Journal Letters 911 (2) L33 (2021)
https://doi.org/10.3847/2041-8213/abf2c7
AstroVaDEr: astronomical variational deep embedder for unsupervised morphological classification of galaxies and synthetic image generation
Ashley Spindler, James E Geach and Michael J SmithMonthly Notices of the Royal Astronomical Society 502 (1) 985 (2021)
https://doi.org/10.1093/mnras/staa3670
Photometric redshift estimation with a convolutional neural network: NetZ
S. Schuldt, S. H. Suyu, R. Cañameras, et al.Astronomy & Astrophysics 651 A55 (2021)
https://doi.org/10.1051/0004-6361/202039945
GalaxyNet: connecting galaxies and dark matter haloes with deep neural networks and reinforcement learning in large volumes
Benjamin P Moster, Thorsten Naab, Magnus Lindström and Joseph A O’LearyMonthly Notices of the Royal Astronomical Society 507 (2) 2115 (2021)
https://doi.org/10.1093/mnras/stab1449
12746 245 (2021)
https://doi.org/10.1007/978-3-030-77977-1_19
Benchmarking and scalability of machine-learning methods for photometric redshift estimation
Ben Henghes, Connor Pettitt, Jeyan Thiyagalingam, Tony Hey and Ofer LahavMonthly Notices of the Royal Astronomical Society 505 (4) 4847 (2021)
https://doi.org/10.1093/mnras/stab1513
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
Twenty-First-Century Statistical and Computational Challenges in Astrophysics
Eric D. Feigelson, Rafael S. de Souza, Emille E.O. Ishida and Gutti Jogesh BabuAnnual Review of Statistics and Its Application 8 (1) 493 (2021)
https://doi.org/10.1146/annurev-statistics-042720-112045
QSO photometric redshifts using machine learning and neural networks
S J Curran, J P Moss and Y C PerrottMonthly Notices of the Royal Astronomical Society 503 (2) 2639 (2021)
https://doi.org/10.1093/mnras/stab485
Intelligent Astrophysics
Lars Doorenbos, Stefano Cavuoti, Massimo Brescia, Antonio D’Isanto and Giuseppe LongoEmergence, Complexity and Computation, Intelligent Astrophysics 39 197 (2021)
https://doi.org/10.1007/978-3-030-65867-0_9
A machine learning approach to galaxy properties: joint redshift–stellar mass probability distributions with Random Forest
S Mucesh, W G Hartley, A Palmese, et al.Monthly Notices of the Royal Astronomical Society 502 (2) 2770 (2021)
https://doi.org/10.1093/mnras/stab164
SILVERRUSH X: Machine Learning-aided Selection of 9318 LAEs at z = 2.2, 3.3, 4.9, 5.7, 6.6, and 7.0 from the HSC SSP and CHORUS Survey Data
Yoshiaki Ono, Ryohei Itoh, Takatoshi Shibuya, Masami Ouchi, Yuichi Harikane, Satoshi Yamanaka, Akio K. Inoue, Toshiyuki Amagasa, Daichi Miura, Maiki Okura, Kazuhiro Shimasaku, Ikuru Iwata, Yoshiaki Taniguchi, Seiji Fujimoto, Masanori Iye, Anton T. Jaelani, Nobunari Kashikawa, Shotaro Kikuchihara, Satoshi Kikuta, Masakazu A. R. Kobayashi, Haruka Kusakabe, Chien-Hsiu Lee, Yongming Liang, Yoshiki Matsuoka, Rieko Momose, et al.The Astrophysical Journal 911 (2) 78 (2021)
https://doi.org/10.3847/1538-4357/abea15
Intervening or associated? Machine learning classification of redshifted H i 21-cm absorption
S J CurranMonthly Notices of the Royal Astronomical Society 506 (1) 1548 (2021)
https://doi.org/10.1093/mnras/stab1865
Deep transfer learning for the classification of variable sources
Dae-Won Kim, Doyeob Yeo, Coryn A. L. Bailer-Jones and Giyoung LeeAstronomy & Astrophysics 653 A22 (2021)
https://doi.org/10.1051/0004-6361/202140369
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 LarrsonThe Astrophysical Journal 920 (2) 118 (2021)
https://doi.org/10.3847/1538-4357/ac1748
Quantifying Non-parametric Structure of High-redshift Galaxies with Deep Learning
C. Tohill, L. Ferreira, C. J. Conselice, S. P. Bamford and F. FerrariThe Astrophysical Journal 916 (1) 4 (2021)
https://doi.org/10.3847/1538-4357/ac033c
Machine-learning computation of distance modulus for local galaxies
A. A. Elyiv, O. V. Melnyk, I. B. Vavilova, D. V. Dobrycheva and V. E. KarachentsevaAstronomy & Astrophysics 635 A124 (2020)
https://doi.org/10.1051/0004-6361/201936883
Probabilistic neural networks for fluid flow surrogate modeling and data recovery
Romit Maulik, Kai Fukami, Nesar Ramachandra, Koji Fukagata and Kunihiko TairaPhysical Review Fluids 5 (10) (2020)
https://doi.org/10.1103/PhysRevFluids.5.104401
Photometric redshift estimation using ExtraTreesRegressor: Galaxies and quasars from low to very high redshifts
Moonzarin Reza and Mohammad Ariful HaqueAstrophysics and Space Science 365 (3) (2020)
https://doi.org/10.1007/s10509-020-03758-w
PhotoWeb redshift: boosting photometric redshift accuracy with large spectroscopic surveys
M. Shuntov, J. Pasquet, S. Arnouts, et al.Astronomy & Astrophysics 636 A90 (2020)
https://doi.org/10.1051/0004-6361/201937382
Quantifying uncertainties on fission fragment mass yields with mixture density networks
A E Lovell, A T Mohan and P TalouJournal of Physics G: Nuclear and Particle Physics 47 (11) 114001 (2020)
https://doi.org/10.1088/1361-6471/ab9f58
294 (2020)
https://doi.org/10.1109/VCIP49819.2020.9301849
Galaxy morphological classification in deep-wide surveys via unsupervised machine learning
G Martin, S Kaviraj, A Hocking, S C Read and J E GeachMonthly Notices of the Royal Astronomical Society 491 (1) 1408 (2020)
https://doi.org/10.1093/mnras/stz3006
Augmenting machine learning photometric redshifts with Gaussian mixture models
P W Hatfield, I A Almosallam, M J Jarvis, et al.Monthly Notices of the Royal Astronomical Society 498 (4) 5498 (2020)
https://doi.org/10.1093/mnras/staa2741
Conditional density estimation tools in python and R with applications to photometric redshifts and likelihood-free cosmological inference
N. Dalmasso, T. Pospisil, A.B. Lee, et al.Astronomy and Computing 30 100362 (2020)
https://doi.org/10.1016/j.ascom.2019.100362
On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars
Sara Jamal and Joshua S. BloomThe Astrophysical Journal Supplement Series 250 (2) 30 (2020)
https://doi.org/10.3847/1538-4365/aba8ff
The PAU Survey: background light estimation with deep learning techniques
L Cabayol-Garcia, M Eriksen, A Alarcón, et al.Monthly Notices of the Royal Astronomical Society 491 (4) 5392 (2020)
https://doi.org/10.1093/mnras/stz3274
Assessing the performance of LTE and NLTE synthetic stellar spectra in a machine learning framework
Spencer Bialek, Sébastien Fabbro, Kim A Venn, et al.Monthly Notices of the Royal Astronomical Society 498 (3) 3817 (2020)
https://doi.org/10.1093/mnras/staa2582
Euclid preparation
G. Desprez, S. Paltani, J. Coupon, et al.Astronomy & Astrophysics 644 A31 (2020)
https://doi.org/10.1051/0004-6361/202039403