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

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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)
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Machine learning technique for morphological classification of galaxies from the SDSS. III. The CNN image-based inference of detailed features

V. KHRAMTSOV, I. B. VAVILOVA, D. V. DOBRYCHEVA, et al.
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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.
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Predicting the Spectrum of UGC 2885, Rubin’s Galaxy with Machine Learning

Benne W. Holwerda, John F. Wu, William C. Keel, Jason Young, Ren Mullins, Joannah Hinz, K. E. Saavik Ford, Pauline Barmby, Rupali Chandar, Jeremy Bailin, Josh Peek, Tim Pickering and Torsten Böker
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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

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The HectoMAP Redshift Survey: First Data Release

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Twenty-First-Century Statistical and Computational Challenges in Astrophysics

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Quantifying Non-parametric Structure of High-redshift Galaxies with Deep Learning

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J-PAS: Measuring emission lines with artificial neural networks

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

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Improving the reliability of photometric redshift with machine learning

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https://doi.org/10.1093/mnras/stab2334

Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours

Matthew C Chan and John P Stott
Monthly Notices of the Royal Astronomical Society 503 (4) 6078 (2021)
https://doi.org/10.1093/mnras/stab858

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

Estimation of Photometric Redshifts. I. Machine-learning Inference for Pan-STARRS1 Galaxies Using Neural Networks

Joongoo Lee and Min-Su Shin
The Astronomical Journal 162 (6) 297 (2021)
https://doi.org/10.3847/1538-3881/ac2e96

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

Weak-lensing Mass Reconstruction of Galaxy Clusters with a Convolutional Neural Network

Sungwook E. 성욱 Hong 홍, Sangnam Park, M. James Jee, Dongsu Bak and Sangjun Cha
The Astrophysical Journal 923 (2) 266 (2021)
https://doi.org/10.3847/1538-4357/ac3090

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

The optical luminosity function of LOFAR radio-selected quasars at 1.4 ≤ z ≤ 5.0 in the NDWFS-Boötes field

E. Retana-Montenegro and H. J. A. Röttgering
Astronomy & Astrophysics 636 A12 (2020)
https://doi.org/10.1051/0004-6361/201936577

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

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

On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars

Sara Jamal and Joshua S. Bloom
The Astrophysical Journal Supplement Series 250 (2) 30 (2020)
https://doi.org/10.3847/1538-4365/aba8ff

The Line-of-Sight Analysis of Spatial Distribution of Galaxies in the COSMOS2015 Catalogue

Maxim Nikonov, Mikhail Chekal, Stanislav Shirokov, Andrey Baryshev and Vladimir Gorokhov
Universe 6 (11) 215 (2020)
https://doi.org/10.3390/universe6110215