  Deep learning algorithms often are trained and deployed on different
datasets. Any systematic difference between the training and a test dataset may
degrade the algorithm performance--what is known as the domain shift problem.
This issue is prevalent in many scientific domains where algorithms are trained
on simulated data but applied to real-world datasets. Typically, the domain
shift problem is solved through various domain adaptation methods. However,
these methods are often tailored for a specific downstream task and may not
easily generalize to different tasks. This work explores the feasibility of
using an alternative way to solve the domain shift problem that is not specific
to any downstream algorithm. The proposed approach relies on modern Unpaired
Image-to-Image translation techniques, designed to find translations between
different image domains in a fully unsupervised fashion. In this study, the
approach is applied to a domain shift problem commonly encountered in Liquid
Argon Time Projection Chamber (LArTPC) detector research when seeking a way to
translate samples between two differently distributed detector datasets
deterministically. This translation allows for mapping real-world data into the
simulated data domain where the downstream algorithms can be run with much less
domain-shift-related degradation. Conversely, using the translation from the
simulated data in a real-world domain can increase the realism of the simulated
dataset and reduce the magnitude of any systematic uncertainties. We adapted
several UI2I translation algorithms to work on scientific data and demonstrated
the viability of these techniques for solving the domain shift problem with
LArTPC detector data. To facilitate further development of domain adaptation
techniques for scientific datasets, the "Simple Liquid-Argon Track Samples"
dataset used in this study also is published.
