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Stellar spectral classification is a fundamental tool of modern astronomy,providing insight into physical characteristics such as effective temperature,surface gravity, and metallicity. Accurate and fast spectral typing is anintegral need for large all-sky spectroscopic surveys like the SDSS and LAMOST.Here, we present the next version of PyHammer, stellar spectral classificationsoftware that uses optical spectral templates and spectral line indexmeasurements. PyHammer v2.0 extends the classification power to include carbon(C) stars, DA white dwarf (WD) stars, and also double-lined spectroscopicbinaries (SB2). This release also includes a new empirical library ofluminosity-normalized spectra that can be used to flux calibrate observedspectra, or to create synthetic SB2 spectra. We have generated physicallyreasonable SB2 combinations as templates, adding to PyHammer the ability tospectrally type SB2s. We test classification success rates on SB2 spectra,generated from the SDSS, across a wide range of spectral types andsignal-to-noise ratios. Within the defined range of pairings described, morethan $95\%$ of SB2s are correctly classified.

Classifying Single Stars and Spectroscopic Binaries Using Optical Stellar Templates