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Cited article:

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The Astrophysical Journal 977 (1) 31 (2024)
https://doi.org/10.3847/1538-4357/ad88f5

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

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Where’s Swimmy?: Mining unique color features buried in galaxies by deep anomaly detection using Subaru Hyper Suprime-Cam data

Takumi S Tanaka, Rhythm Shimakawa, Kazuhiro Shimasaku, Yoshiki Toba, Nobunari Kashikawa, Masayuki Tanaka and Akio K Inoue
Publications of the Astronomical Society of Japan 74 (1) 1 (2022)
https://doi.org/10.1093/pasj/psab105

YOUNG Star detrending for Transiting Exoplanet Recovery (YOUNGSTER) – II. Using self-organizing maps to explore young star variability in sectors 1–13 of TESS data

Matthew P Battley, David J Armstrong and Don Pollacco
Monthly Notices of the Royal Astronomical Society 511 (3) 4285 (2022)
https://doi.org/10.1093/mnras/stac278

Identifying Outliers in Astronomical Images with Unsupervised Machine Learning

Yang Han, Zhiqiang Zou, Nan Li and Yanli Chen
Research in Astronomy and Astrophysics 22 (8) 085006 (2022)
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GUASOM: an adaptive visualization tool for unsupervised clustering in spectrophotometric astronomical surveys

M. A. Álvarez, C. Dafonte, M. Manteiga, D. Garabato and R. Santoveña
Neural Computing and Applications 34 (3) 1993 (2022)
https://doi.org/10.1007/s00521-021-06510-9

Intelligent Astrophysics

Lars Doorenbos, Stefano Cavuoti, Massimo Brescia, Antonio D’Isanto and Giuseppe Longo
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Density-based outlier scoring on Kepler data

Daniel K Giles and Lucianne Walkowicz
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A Blended Artificial Intelligence Approach for Spectral Classification of Stars in Massive Astronomical Surveys

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Entropy 22 (5) 518 (2020)
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Detecting outliers in astronomical images with deep generative networks

Berta Margalef-Bentabol, Marc Huertas-Company, Tom Charnock, et al.
Monthly Notices of the Royal Astronomical Society 496 (2) 2346 (2020)
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Identifying strong lenses with unsupervised machine learning using convolutional autoencoder

Robert B Metcalf, Simon Dye, Alfonso Aragón-Salamanca, et al.
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Increasing the share of correct clustering of characteristic signal with random losses in self-organizing maps

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Distributed Fast Self-Organized Maps for Massive Spectrophotometric Data Analysis †

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https://doi.org/10.3390/s18051419

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Lecture Notes in Computer Science, Neural Information Processing 9950 137 (2016)
https://doi.org/10.1007/978-3-319-46681-1_17

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Monthly Notices of the Royal Astronomical Society 455 (4) 4289 (2016)
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MAPPING THE GALAXY COLOR–REDSHIFT RELATION: OPTIMAL PHOTOMETRIC REDSHIFT CALIBRATION STRATEGIES FOR COSMOLOGY SURVEYS

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The Astrophysical Journal 813 (1) 53 (2015)
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GUASOM: Gaia Utility for Analysis and Knowledge Discovery based on Self Organizing Maps

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https://doi.org/10.1051/eas/1567073