Deep learning (DL) techniques have broad applications in science, especially in seeking to streamline the pathway to potential solutions and discoveries. Frequently, however, DL models are trained on the results of simulation yet applied to real experimental data. As such, any systematic differences between the simulated and real data may degrade the model's performance -- an effect known as "domain shift." This work studies a toy model of the systematic differences between simulated and real data. It presents a fully unsupervised, task-agnostic method to reduce differences between two systematically different samples. The method is based on the recent advances in unpaired image-to-image translation techniques and is validated on two sets of samples of simulated Liquid Argon Time Projection Chamber (LArTPC) detector events, created to illustrate common systematic differences between the simulated and real data in a controlled way. LArTPC-based detectors represent the next-generation particle detectors, producing unique high-resolution particle track data. This work open-sources the generated LArTPC data set, called Simple Liquid-Argon Track Samples (or SLATS), allowing researchers from diverse domains to study the LArTPC-like data for the first time. The code and trained models are available at https://github.com/LS4GAN/uvcgan4slats.

翻译：[译文摘要] 深度学习技术具有广阔的科研应用前景，尤其在简化潜在解决方案与创新发现的路径方面。然而，深度学习模型通常基于仿真结果进行训练，却应用于真实实验数据。仿真数据与真实数据之间的系统性差异可能导致模型性能下降——这种效应被称为"域偏移"。本研究构建了一个模拟真实数据与仿真数据之间系统性差异的简化模型，并提出了一种完全无监督、任务无关的方法来减少两个系统性不同样本间的差异。该方法基于近期非配对图像到图像翻译技术的进展，并在两组模拟液氩时间投影室（LArTPC）探测器事件样本上进行了验证——这些样本旨在可控条件下揭示仿真数据与真实数据之间的常见系统性差异。基于LArTPC的探测器作为新一代粒子探测器，可生成独特的高分辨率粒子径迹数据。本研究开源了生成的LArTPC数据集（名为简单液氩径迹样本，简称SLATS），使不同领域的研究人员首次能够研究类LArTPC数据。相关代码与预训练模型已发布于https://github.com/LS4GAN/uvcgan4slats。