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We present a new approach to capturing the broad diversity of emission lineand continuum properties in quasar spectra. We identify populations ofspectrally similar quasars through pixel-level clustering on 12,968 highsignal-to-noise spectra from the Sloan Digital Sky Survey (SDSS) in theredshift range of $1.57<z<2.4$. Our clustering analysis finds 396 quasarspectra that are not assigned to any population, 15 misclassified spectra, andsix quasars with incorrect redshifts. We compress the quasar populations into alibrary of 684 high signal-to-noise composite spectra, anchored in redshiftspace by the Mg II emission line. Principal component analysis on the libraryresults in an eigenspectrum basis spanning 1067 to 4007 $\r{A}$. We modelindependent samples of SDSS quasar spectra with the eigenbasis, allowing for afree redshift parameter. Our models achieve a median reduced chi-squared onnon-BAL quasar spectra that is reduced by 8.5% relative to models using theeigenspectra from the SDSS spectroscopic pipeline. A significant contributionto the relative improvement is from the ability to reconstruct the range ofemission line variation. The redshift estimates from our model are consistentwith the Mg II emission line redshift with an average offset that displays51.4% less redshift-dependent variation relative to the SDSS eigenspectra. Ourmethod for developing quasar spectra models can improve automatedclassification and predict the intrinsic spectrum in regions affected byintervening absorbers such as Ly$\alpha$, C IV, and Mg II, thus benefitingstudies of large-scale structure.

Modeling the Spectral Diversity of Quasars in the Sixteenth Data Release from the Sloan Digital Sky Survey