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DOE OSTI · 2338236

Score-based diffusion models for generating liquid argon time projection chamber images

Imani, Zeviel (ORCID:0009000770065204)·Wongjirad, Taritree·Aeron, Shuchin (ORCID:0000000210499795)
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Abstract

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. Published by the American Physical Society 2024

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Imani, Zeviel (ORCID:0009000770065204), Wongjirad, Taritree, Aeron, Shuchin (ORCID:0000000210499795). 2024-04-18. Score-based diffusion models for generating liquid argon time projection chamber images. https://doi.org/10.1103/physrevd.109.072011

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