Dimensionality Reduction of SDSS Spectra with Variational Autoencoders

• Published in: The Astronomical journal 2020-07, Vol.160 (1), p.45
• Main Authors: Portillo, Stephen K. N., Parejko, John K., Vergara, Jorge R., Connolly, Andrew J.
• Format: Article
• Language: English
• Subjects: Astroinformatics; Astronomical data; Astronomy; ASTROPHYSICS, COSMOLOGY AND ASTRONOMY; Celestial bodies; Dimensionality reduction; Energy management; EXPLORATION; GALAXIES; Galaxy classification systems; Neural networks; NONLINEAR PROBLEMS; Outliers (statistics); Parameters; Photovoltaic cells; Principal components analysis; Reduction; RESOLUTION; Sky surveys (astronomy); SPACE; SPECTRA; Spectroscopy; STARS; Stars & galaxies
• ISSN: 0004-6256; 1538-3881; 1538-3881
• DOI: 10.3847/1538-3881/ab9644

High-resolution galaxy spectra contain much information about galactic physics, but the high dimensionality of these spectra makes it difficult to fully utilize the information they contain. We apply variational autoencoders (VAEs), a nonlinear dimensionality reduction technique, to a sample of spectra from the Sloan Digital Sky Survey (SDSS). In contrast to principal component analysis (PCA), a widely used technique, VAEs can capture nonlinear relationships between latent parameters and the data. We find that a VAE can reconstruct the SDSS spectra well with only six latent parameters, outperforming PCA with the same number of components. Different galaxy classes are naturally separated in this latent space, without class labels having been given to the VAE. The VAE latent space is interpretable because the VAE can be used to make synthetic spectra at any point in latent space. For example, making synthetic spectra along tracks in latent space yields sequences of realistic spectra that interpolate between two different types of galaxies. Using the latent space to find outliers may yield interesting spectra: in our small sample, we immediately find unusual data artifacts and stars misclassified as galaxies. In this exploratory work, we show that VAEs create compact, interpretable latent spaces that capture nonlinear features of the data. While a VAE takes substantial time to train ( 1 day for 48,000 spectra), once trained, VAEs can enable the fast exploration of large astronomical data sets.