  The advancements in stellar spectroscopy data acquisition have made it
necessary to accomplish similar improvements in efficient data analysis
techniques. Current automated methods for analyzing spectra are either (a)
data-driven, which requires prior knowledge of stellar parameters and elemental
abundances, or (b) based on theoretical synthetic models that are susceptible
to the gap between theory and practice. In this study, we present a hybrid
generative domain adaptation method that turns simulated stellar spectra into
realistic spectra by applying unsupervised learning to large spectroscopic
surveys. We apply our technique to the APOGEE H-band spectra at R=22,500 and
the Kurucz synthetic models. As a proof of concept, two case studies are
presented. The first of which is the calibration of synthetic data to become
consistent with observations. To accomplish this, synthetic models are morphed
into spectra that resemble observations, thereby reducing the gap between
theory and observations. Fitting the observed spectra shows an improved average
reduced $\chi_R^2$ from 1.97 to 1.22, along with a reduced mean residual from
0.16 to -0.01 in normalized flux. The second case study is the identification
of the elemental source of missing spectral lines in the synthetic modelling. A
mock dataset is used to show that absorption lines can be recovered when they
are absent in one of the domains. This method can be applied to other fields,
which use large data sets and are currently limited by modelling accuracy. The
code used in this study is made publicly available on github.
