Score-based Diffusion Models for Generating Liquid Argon Time Projection Chamber Images

TL;DR

This paper demonstrates high-fidelity generation of Liquid Argon Time Projection Chamber images using a score-based diffusion model, showing transferability of natural image methods to sparse, dense scientific data.

Contribution

First application of score-based diffusion models to generate LArTPC images, adapting methods from natural image processing to scientific data with unique sparsity and density characteristics.

Findings

High-quality LArTPC image generation achieved

Methods transfer successfully from natural images to scientific data

Evaluation shows strong fidelity and distribution matching

Abstract

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.