Towards Designing and Exploiting Generative Networks for Neutrino Physics Experiments using Liquid Argon Time Projection Chambers

Paul Lutkus; Taritree Wongjirad; Shuchin Aeron

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arXiv · 2204.02496

Towards Designing and Exploiting Generative Networks for Neutrino Physics Experiments using Liquid Argon Time Projection Chambers

Paul Lutkus·Taritree Wongjirad·Shuchin Aeron

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Abstract

In this paper, we show that a hybrid approach to generative modeling via combining the decoder from an autoencoder together with an explicit generative model for the latent space is a promising method for producing images of particle trajectories in a liquid argon time projection chamber (LArTPC). LArTPCs are a type of particle physics detector used by several current and future experiments focused on studies of the neutrino. We implement a Vector-Quantized Variational Autoencoder (VQ-VAE) and PixelCNN which produces images with LArTPC-like features and introduce a method to evaluate the quality of the images using a semantic segmentation that identifies important physics-based features.

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Paul Lutkus, Taritree Wongjirad, Shuchin Aeron. 2022-04-05. Towards Designing and Exploiting Generative Networks for Neutrino Physics Experiments using Liquid Argon Time Projection Chambers. https://arxiv.org/abs/2204.02496

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