Improving Automated Spectral Classifications Through Visual Inspections of Outliers

One of the best ways to improve our knowledge of the physical cosmology of our universe is through observation of quasar spectra and redshift classification. My work includes visually inspecting Sloan Digital Sky Survey (SDSS) quasar spectra in order to improve cosmological parameters, improve the precision in clustering measurements for Baryon Acoustic Oscillations (BAOs) and improve automated computer pipelines. This is achieved by manually classifying the object, redshift, and any notable features in the quasar spectra we inspect, noting any peculiar cases that might contribute to revising automated spectral classifications and cosmology research. This presentation will provide the methods I have taken in visual inspection and classifications, outliers that we have found significant or intriguing, and examples of defining features that can aid the pursuits of cosmology.