论文标题
旨在使用液体氩时间投影室设计和利用中微子物理实验的生成网络
Towards Designing and Exploiting Generative Networks for Neutrino Physics Experiments using Liquid Argon Time Projection Chambers
论文作者
论文摘要
在本文中，我们表明，通过将自动编码器的解码器与潜在空间的显式生成模型相结合的混合方法是一种有前途的方法，是在液体氩时间投影室（LARTPC）中产生粒子轨迹图像的有前途方法。 LARTPC是一种粒子物理探测器，该粒子物理检测器由几个目前和将来的实验用于中微子的研究。我们实施了矢量量化的变分自动编码器（VQ-VAE）和PixelCNN，该图像具有类似LARTPC的特征的图像，并介绍了一种使用语义细分来评估图像质量的方法，该语义细分识别重要的基于物理学的特征。
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.
