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Euclid preparation – XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images
Euclid Collaboration
Astrophysical Sciences
Research output
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Contribution to journal
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Article
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peer-review
18
Scopus citations
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Dive into the research topics of 'Euclid preparation – XXIII. Derivation of galaxy physical properties with deep machine learning using mock fluxes and H-band images'. Together they form a unique fingerprint.
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Keyphrases
Physical Properties
100%
Galaxies
100%
H-band
100%
Euclid
100%
Deep Machine Learning
100%
Star Formation Rate
50%
Stellar Masses
50%
Deep Learning
50%
Convolutional Neural Network
33%
Vera C. Rubin Observatory
33%
Neural Network
16%
Parameter Space
16%
Spectral Energy Distribution
16%
Training Samples
16%
Machine Learning Techniques
16%
Roman
16%
Next Generation Telescope
16%
Distribution Fitting
16%
Normalized Error
16%
Physics
Star Formation Rate
100%
Stellar Mass
100%
Machine Learning
100%
Deep Learning
100%
Convolutional Neural Network
66%
Spectral Energy Distribution
33%
Neural Network
33%