Diffusion-Based Generative Modeling for LArTPC Images

Advances in machine learning have changed how we understand data, especially the recent advent of generative modeling, which allows the creation of novel examples from a given dataset. Seeking to make use of these new methods, I applied a modern diffusion-based generative model (Yang Song & Stefano Ermon, 2019) to the PILArNet public dataset. The data consists of 2D images of simulated particle tracks and showers detected within a Liquid Argon Time Projection Chamber (LArTPC). In this presentation, I will outline the methodology used in the algorithm, demonstrate the quality of the generated images, and provide insight into the future applicability of this approach.