  The present study presents a novel application for normalizing flows for
domain adaptation. The study investigates the ability of flow based neural
networks to improve signal extraction of $\Lambda$ Hyperons at CLAS12.
Normalizing Flows can help model complex probability density functions that
describe physics processes, enabling uses such as event generation. $\Lambda$
signal extraction has been improved through the use of classifier networks, but
differences in simulation and data domains limit classifier performance; this
study utilizes the flows for domain adaptation between Monte Carlo simulation
and data. We were successful in training a flow network to transform between
the latent physics space and a normal distribution. We also found that applying
the flows lessened the dependence of the figure of merit on the cut on the
classifier output, meaning that there was a broader range where the cut results
in a similar figure of merit.
