For the first time, we show high-fidelity generation of Liquid Argon Time Projection Chamber (LArTPC-like) data using a generative neural network. This demonstrates that methods developed for natural images do transfer to LArTPC-produced images, which, in contrast to natural images, are globally sparse but locally dense. We present the score-based diffusion method employed. We evaluate the fidelity of the generated images using several quality metrics, including modified measures used to evaluate natural images, comparisons between high-dimensional distributions, and comparisons relevant to LArTPC experiments.

中文翻译：

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用于生成液氩时间投影室图像的基于分数的扩散模型

我们首次使用生成神经网络展示了液氩时间投影室（类似 LArTPC）数据的高保真生成。这表明为自然图像开发的方法确实可以转移到 LArTPC 生成的图像，与自然图像相比，这些图像全局稀疏但局部密集。我们提出了采用的基于分数的扩散方法。我们使用多种质量指标来评估生成图像的保真度，包括用于评估自然图像的修改度量、高维分布之间的比较以及与 LArTPC 实验相关的比较。