  Optical spectra of galaxies and quasars from large cosmological surveys are
used to measure redshifts and infer distances. They are also rich with
information on the intrinsic properties of these astronomical objects. However,
their physical interpretation can be challenging due to the substantial number
of degrees of freedom, various sources of noise, and degeneracies between
physical parameters that cause similar spectral characteristics. To gain deeper
insights into these degeneracies, we apply two unsupervised machine learning
frameworks to a sample from the Sloan Digital Sky Survey data release 16 (SDSS
DR16). The first framework is a Probabilistic Auto-Encoder (PAE), a two-stage
deep learning framework consisting of a data compression stage from 1000
elements to 10 parameters and a density estimation stage. The second framework
is a Uniform Manifold Approximation and Projection (UMAP), which we apply to
both the uncompressed and compressed data. Exploring across regions on the
compressed data UMAP, we construct sequences of stacked spectra which show a
gradual transition from star-forming galaxies with narrow emission lines and
blue spectra to passive galaxies with absorption lines and red spectra.
Focusing on galaxies with broad emission lines produced by quasars, we find a
sequence with varying levels of obscuration caused by cosmic dust. The
experiments we present here inform future applications of neural networks and
dimensionality reduction algorithms for large astronomical spectroscopic
surveys.
