  For the first time, we show high-fidelity generation of 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.
