Speaker
Description
Generating simulation data for future and current LArTPC experiments requires addressing several challenges, such as reducing computation time and the expression of detector model uncertainties. Inspired by the success of recently developed generative models to produce complex, high-dimensional data such as natural images, we are exploring how these methods might be applied to LArTPCs. Initial studies of denoising diffusion probabilistic models (DDPMs) have been able to successfully reproduce simulated 2D LArTPC images. This generative model approach produces high-fidelity images of track and shower particle event types that demonstrate realistic physics metrics. We continue to explore various applications for physics analyses across different datasets. In this talk, I will discuss our methodology, quality metrics, and direction of future work with generative modeling for LArTPC physics.
| Type of contribution | Talk: 15 minutes. |
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