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Cited article:

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Maximum Correntropy Based Spectral Redshift Estimation for Spectral Redshift Navigation

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IEEE Transactions on Instrumentation and Measurement 72 1 (2023)
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Estimating galaxy redshift in radio-selected datasets using machine learning

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Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

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Preliminary Results of Using k-nearest-neighbor Regression to Estimate the Redshift of Radio-selected Data Sets

Kieran J. Luken, Ray P. Norris and Laurence A. F. Park
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Deep learning approach for classifying, detecting and predicting photometric redshifts of quasars in the Sloan Digital Sky Survey stripe 82

J. Pasquet-Itam and J. Pasquet
Astronomy & Astrophysics 611 A97 (2018)
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Sacrificing information for the greater good: how to select photometric bands for optimal accuracy

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PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

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https://doi.org/10.3847/0067-0049/225/2/31