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This document discusses the application of variational autoencoders (VAEs) for dimensionality reduction of galaxy spectra from the Sloan Digital Sky Survey (SDSS). VAEs outperform traditional Principal Component Analysis (PCA) by effectively capturing non-linear relationships and enabling the generation of synthetic spectra, while also facilitating the identification of outliers. The study highlights the potential of VAEs in creating compact and interpretable latent spaces that enhance the analysis of compl

Author

Vyom Rathore

Language

EN