EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues A&A, 609 (2018) A111 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:

LSTM-MDNz: Estimating Quasar Photometric Redshifts with an LSTM-augmented Mixture Density Network

Jianzhen Chen, Zhijian Luo, Liping Fu, Zhu Chen, Hubing Xiao, Shaohua Zhang and Chenggang Shu
The Astrophysical Journal Supplement Series 282 (2) 46 (2026)
https://doi.org/10.3847/1538-4365/ae2eb2

Predicting large-scale cosmological structure evolution with generative adversarial network-based autoencoders

Marion Ullmo, Nabila Aghanim, Aurélien Decelle and Miguel Aragon-Calvo
Astronomy & Astrophysics 706 A124 (2026)
https://doi.org/10.1051/0004-6361/202449845

DeepRed: an architecture for redshift estimation

Alessandro Meroni, Nicolò Oreste Pinciroli Vago and Piero Fraternali
Neural Computing and Applications 38 (6) (2026)
https://doi.org/10.1007/s00521-026-11932-4

Euclid Quick Data Release (Q1)

L. Bazzanini, G. Angora, P. Bergamini, M. Meneghetti, P. Rosati, A. Acebron, C. Grillo, M. Lombardi, R. Ratta, M. Fogliardi, G. Di Rosa, D. Abriola, M. D’Addona, G. Granata, L. Leuzzi, A. Mercurio, J. M. Palencia, S. Schuldt, E. Vanzella, C. Tortora, B. Altieri, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, et al.
Astronomy & Astrophysics 711 A156 (2026)
https://doi.org/10.1051/0004-6361/202557590

Euclid Quick Data Release (Q1)

M. Tucci, S. Paltani, W. G. Hartley, F. Dubath, N. Morisset, M. Bolzonella, S. Fotopoulou, F. Tarsitano, C. Saulder, L. Pozzetti, A. Enia, Y. Kang, H. Degaudenzi, R. Saglia, M. Salvato, O. Ilbert, S. A. Stanford, W. Roster, F. J. Castander, A. Humphrey, H. Landt, M. Selwood, G. Stevens, N. Aghanim, B. Altieri, et al.
Astronomy & Astrophysics 711 A5 (2026)
https://doi.org/10.1051/0004-6361/202554588

Photometric Redshift Probability Density Functions via Neural Network Classification for DESI Legacy Imaging Surveys and Pan-STARRS

Da-Chuan Tian, Zhong-Lue Wen and Jun-Qing Xia
The Astrophysical Journal Supplement Series 285 (1) 8 (2026)
https://doi.org/10.3847/1538-4365/ae6641

​​​​​​A Review of Machine Learning in Astronomical Plate Digitization and Survey Data

Tauseef Ahmad Zafar and René Hudec
Publications of the Astronomical Society of the Pacific 138 (6) 061001 (2026)
https://doi.org/10.1088/1538-3873/ae7205

Probabilistic estimation of galactic inclinations: Inferring calibrated uncertainties using a network trained on cosmological simulations

F. Schweder, S. Trujillo-Gomez and K. Polsterer
Astronomy and Computing 57 101132 (2026)
https://doi.org/10.1016/j.ascom.2026.101132

Search for quasar pairs with Gaia astrometric data

Xingyu Zhu, Qihang Chen, Liang Jing, Zhuojun Deng, Jun-Qing Xia, Yanxia Zhang and Jianghua Wu
Astronomy & Astrophysics 710 A191 (2026)
https://doi.org/10.1051/0004-6361/202658953

Co-SOM: Co-training for photometric redshift estimation using Self-Organizing Maps

A. Callejas-Tavera, E. Molino-Minero-Re and O. Valenzuela
Astronomy and Computing 54 101019 (2026)
https://doi.org/10.1016/j.ascom.2025.101019

Quasar Photometric Redshift Estimation by Transferring Spectroscopic Knowledge to Image Features

Bixin Dong, Feng Zhang, Siming Ding, Huiyu Chen, Zhiqiang Zou, A-Li Luo and Xiao Kong
Publications of the Astronomical Society of the Pacific 138 (5) 054509 (2026)
https://doi.org/10.1088/1538-3873/ae6a71

A Value-added Physical Properties Catalog for Low-redshift Galaxies from DESI Legacy Imaging Surveys DR10

Shirui Wei, Changhua Li, Yanxia Zhang, Chenzhou Cui, Jinghang Shi, Wujun Shao, Zihan Kang, Yongheng Zhao and Maoyuan Huang
The Astrophysical Journal Supplement Series 284 (2) 45 (2026)
https://doi.org/10.3847/1538-4365/ae6242

Photo-z Estimation with Normalizing Flow

Yiming Ren, Kwan Chuen Chan, Le Zhang, Yin Li, Haolin Zhang, Ruiyu Song, Yan Gong, Xian-Min Meng and Xingchen Zhou
The Astrophysical Journal 997 (1) 65 (2026)
https://doi.org/10.3847/1538-4357/ae201d

Is the “Known” Enough? An Integrated Machine Learning Framework for Eclipsing Binary Classification and Parameter Estimation Based on Well-characterized Systems

Burak Ulaş
Publications of the Astronomical Society of the Pacific 138 (5) 054506 (2026)
https://doi.org/10.1088/1538-3873/ae661f

A Fuzzy and Explainable AI Framework for Comparing Physical and Perceptual Representations in Galaxy Morphology

Gabriel Marín Díaz, Alvaro Manuel Rodriguez-Rodriguez and Eva María Andrés Núñez
AI 7 (5) 159 (2026)
https://doi.org/10.3390/ai7050159

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

E. Tempel, J. Laur, Z. R. Jones, R. Kipper, L. J. Liivamägi, D. Pandey, G. Sakteos, A. Tamm, A. N. Triantafyllaki and T. Tuvikene
Astronomy & Astrophysics 703 A261 (2025)
https://doi.org/10.1051/0004-6361/202553683

Photometric Redshift Forecast for the 7-Dimensional Sky Survey

Eunhee 은희 Ko 고, Myungshin Im, Yujin Yang, Ji Hoon Kim, Seong-Kook Lee and Gregory S.-H. Paek
The Astrophysical Journal 994 (2) 224 (2025)
https://doi.org/10.3847/1538-4357/ae0cb8

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 Park
The Astrophysical Journal Supplement Series 277 (2) 41 (2025)
https://doi.org/10.3847/1538-4365/adb42a

Performance evaluation of efficient interpretable CNN-transformer model for redshift prediction

Snigdha Sen, Ambuj Kumar Pandit, Pavan Chakraborty and Krishna Pratap Singh
Signal, Image and Video Processing 19 (4) (2025)
https://doi.org/10.1007/s11760-025-03855-9

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

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 Fan
Research in Astronomy and Astrophysics 25 (5) 055021 (2025)
https://doi.org/10.1088/1674-4527/adcc7e

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

Fast and accurate stellar mass predictions from broad-band magnitudes with a simple neural network: application to simulated star-forming galaxies

E Elson
RAS Techniques and Instruments 4 (2025)
https://doi.org/10.1093/rasti/rzaf029

ULISSE: Determination of the star formation rate and stellar mass based on the one-shot galaxy imaging technique

Olena Torbaniuk, Lars Doorenbos, Maurizio Paolillo, Stefano Cavuoti, Massimo Brescia and Giuseppe Longo
Astronomy & Astrophysics 701 A162 (2025)
https://doi.org/10.1051/0004-6361/202452704

Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation

Andrew W. Engel, Nell Byler, Adam Tsou, Gautham Narayan, Emmanuel Bonilla and Ian Smith
The Astrophysical Journal 991 (2) 124 (2025)
https://doi.org/10.3847/1538-4357/adf5b8

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 Xun
Astronomy & Astrophysics 694 A271 (2025)
https://doi.org/10.1051/0004-6361/202452813

Photometric redshift predictions with a neural network for DESI quasars

Jeremy Moss, Stephen Curran and Yvette Perrott
Publications of the Astronomical Society of Australia 42 (2025)
https://doi.org/10.1017/pasa.2025.10075

Towards instance-wise calibration: local amortized diagnostics and reshaping of conditional densities (LADaR)

Biprateep Dey, David Zhao, Brett H Andrews, Jeffrey A Newman, Rafael Izbicki and Ann B Lee
Machine Learning: Science and Technology 6 (4) 045058 (2025)
https://doi.org/10.1088/2632-2153/ae1f05

A Brief Review of Unsupervised Machine Learning Algorithms in Astronomy: Dimensionality Reduction and Clustering

Chih-Ting Kuo, Duo Xu and Rachel Friesen
Universe 11 (12) 412 (2025)
https://doi.org/10.3390/universe11120412

Deep Drug Synergy Prediction Network Using Modified Triangular Mutation-Based Differential Evolution

Dilbag Singh, Ahmad Ali Alzubi, Manjit Kaur, Vijay Kumar and Heung-No Lee
IEEE Journal of Biomedical and Health Informatics 29 (1) 669 (2025)
https://doi.org/10.1109/JBHI.2024.3377631

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

Decompositions of the mean continuous ranked probability score

Sebastian Arnold, Eva-Maria Walz, Johanna Ziegel and Tilmann Gneiting
Electronic Journal of Statistics 18 (2) (2024)
https://doi.org/10.1214/24-EJS2316

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 Uchiyama
The Astrophysical Journal 977 (1) 81 (2024)
https://doi.org/10.3847/1538-4357/ad8a67

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-Neila
The Astrophysical Journal 977 (1) 131 (2024)
https://doi.org/10.3847/1538-4357/ad8bbe

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. More
Astronomy & Astrophysics 692 A72 (2024)
https://doi.org/10.1051/0004-6361/202347072

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

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. Nandra
Astronomy & Astrophysics 692 A260 (2024)
https://doi.org/10.1051/0004-6361/202452361

Easy Uncertainty Quantification (EasyUQ): Generating Predictive Distributions from Single-Valued Model Output

Eva-Maria Walz, Alexander Henzi, Johanna Ziegel and Tilmann Gneiting
SIAM Review 66 (1) 91 (2024)
https://doi.org/10.1137/22M1541915

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

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

Unsupervised Domain Adaptation for Constraining Star Formation Histories

Sankalp Gilda, Antoine de Mathelin, Sabine Bellstedt and Guillaume Richard
Astronomy 3 (3) 189 (2024)
https://doi.org/10.3390/astronomy3030012

Exploring galactic properties with machine learning

F. Z. Zeraatgari, F. Hafezianzadeh, Y.-X. Zhang, A. Mosallanezhad and J.-Y. Zhang
Astronomy & Astrophysics 688 A33 (2024)
https://doi.org/10.1051/0004-6361/202348714

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

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. Stringfellow
Astronomy and Computing 49 100886 (2024)
https://doi.org/10.1016/j.ascom.2024.100886

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. Сюняев
Pisʹma v Astronomičeskij žurnal 50 (1) 38 (2024)
https://doi.org/10.31857/S0320010824010031

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 Darc
Monthly Notices of the Royal Astronomical Society 535 (1) 961 (2024)
https://doi.org/10.1093/mnras/stae2390

Photometric Redshift Estimation of Quasars by a Cross-modal Contrast Learning Method

Chen Zhang, Wenyu Wang, Meixia Qu, Bin Jiang and YanXia Zhang
The Astronomical Journal 168 (6) 244 (2024)
https://doi.org/10.3847/1538-3881/ad79f9

CLAP

Qiufan Lin, Hengxin Ruan, Dominique Fouchez, Shupei Chen, Rui Li, Paulo Montero-Camacho, Nicola R. Napolitano, Yuan-Sen Ting and Wei Zhang
Astronomy & Astrophysics 691 A331 (2024)
https://doi.org/10.1051/0004-6361/202349113

Detection of Einstein telescope gravitational wave signals from binary black holes using deep learning

Wathela Alhassan, T Bulik and M Suchenek
Monthly Notices of the Royal Astronomical Society 519 (3) 3843 (2023)
https://doi.org/10.1093/mnras/stac3797

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. Filipović
Publications of the Astronomical Society of Australia 40 (2023)
https://doi.org/10.1017/pasa.2023.39

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 Salvo
Universe 9 (8) 359 (2023)
https://doi.org/10.3390/universe9080359

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. Nelemans
Astronomy & 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 Zhao
Universe 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 Ko
RAS Techniques and Instruments 2 (1) 649 (2023)
https://doi.org/10.1093/rasti/rzad044

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 Chen
Monthly Notices of the Royal Astronomical Society 527 (4) 10557 (2023)
https://doi.org/10.1093/mnras/stad3756

Dealing with imbalanced regression problem for large dataset using scalable Artificial Neural Network

Snigdha Sen, Krishna Pratap Singh and Pavan Chakraborty
New Astronomy 99 101959 (2023)
https://doi.org/10.1016/j.newast.2022.101959

CNN photometric redshifts in the SDSS at r ≤ 20

M Treyer, R Ait Ouahmed, J Pasquet, S Arnouts, E Bertin and D Fouchez
Monthly Notices of the Royal Astronomical Society 527 (1) 651 (2023)
https://doi.org/10.1093/mnras/stad3171

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 Bi
The Astrophysical Journal Supplement Series 264 (2) 41 (2023)
https://doi.org/10.3847/1538-4365/acabc8

Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning

Helen Qu and Masao Sako
The 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

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. Sunyaev
Astronomy Letters 49 (11) 646 (2023)
https://doi.org/10.1134/S1063773723110129

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

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 Simionescu
Monthly Notices of the Royal Astronomical Society 527 (2) 3315 (2023)
https://doi.org/10.1093/mnras/stad3371

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 Wang
Monthly Notices of the Royal Astronomical Society 523 (4) 5799 (2023)
https://doi.org/10.1093/mnras/stad1842

The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys

M. Huertas-Company and F. Lanusse
Publications of the Astronomical Society of Australia 40 (2023)
https://doi.org/10.1017/pasa.2022.55

Mimicking the halo–galaxy connection using machine learning

Natalí S M de Santi, Natália V N Rodrigues, Antonio D Montero-Dorta, L Raul Abramo, Beatriz Tucci and M Celeste Artale
Monthly Notices of the Royal Astronomical Society 514 (2) 2463 (2022)
https://doi.org/10.1093/mnras/stac1469

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

Evaluation of machine learning techniques for forecast uncertainty quantification

Maximiliano A. Sacco, Juan J. Ruiz, Manuel Pulido and Pierre Tandeo
Quarterly Journal of the Royal Meteorological Society 148 (749) 3470 (2022)
https://doi.org/10.1002/qj.4362

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

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 Kembhavi
Monthly Notices of the Royal Astronomical Society 518 (2) 3123 (2022)
https://doi.org/10.1093/mnras/stac3336

Soft-labeling approach along with an ensemble of models for predicting subjective freshness of spinach leaves

Kento Koyama and Suxing Lyu
Computers and Electronics in Agriculture 193 106633 (2022)
https://doi.org/10.1016/j.compag.2021.106633

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

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

Estimation of Photometric Redshifts. II. Identification of Out-of-distribution Data with Neural Networks

Joongoo Lee and Min-Su Shin
The Astronomical Journal 163 (2) 98 (2022)
https://doi.org/10.3847/1538-3881/ac4335

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

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

Deep learning methods for obtaining photometric redshift estimations from images

Ben Henghes, Jeyan Thiyagalingam, Connor Pettitt, Tony Hey and Ofer Lahav
Monthly Notices of the Royal Astronomical Society 512 (2) 1696 (2022)
https://doi.org/10.1093/mnras/stac480

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

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. Filipović
Astronomy and Computing 39 100557 (2022)
https://doi.org/10.1016/j.ascom.2022.100557

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 Wende
IEEE Access 10 101276 (2022)
https://doi.org/10.1109/ACCESS.2022.3207480

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 Wang
The Astrophysical Journal 937 (2) 97 (2022)
https://doi.org/10.3847/1538-4357/ac8c27

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

Deep transfer learning for the classification of variable sources

Dae-Won Kim, Doyeob Yeo, Coryn A. L. Bailer-Jones and Giyoung Lee
Astronomy & Astrophysics 653 A22 (2021)
https://doi.org/10.1051/0004-6361/202140369

Self-supervised Representation Learning for Astronomical Images

Md Abul Hayat, George Stein, Peter Harrington, Zarija Lukić and Mustafa Mustafa
The Astrophysical Journal Letters 911 (2) L33 (2021)
https://doi.org/10.3847/2041-8213/abf2c7

Intervening or associated? Machine learning classification of redshifted H i 21-cm absorption

S J Curran
Monthly Notices of the Royal Astronomical Society 506 (1) 1548 (2021)
https://doi.org/10.1093/mnras/stab1865