arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2204.02496hep-ex
收藏

利用液氩时间投影室设计和开发中微子物理实验生成网络

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

Paul Lutkus, Taritree Wongjirad, Shuchin Aeron

摘要:

在本文中,我们表明,一种混合的方法,通过结合从一个自动编码器的解码器与显式生成模型的潜在空间生成建模是一种很有前途的方法,用于产生图像的粒子轨迹在液氩时间投影室(LArTPC)。LArTPC是一种粒子物理探测器,用于当前和未来的几个中微子研究实验。我们实现了一个矢量量化变分自动编码器(VQ-VAE)和PixelCNN,它产生具有LArTPC类特征的图像,并引入了一种使用语义分割来评估图像质量的方法,该方法可以识别重要的基于物理的特征。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑