论文标题
使用神经网络拒绝贝加尔-GVD数据中的噪声
Rejecting noise in Baikal-GVD data with neural networks
论文作者
论文摘要
Baikal-GVD是一个大型（$ \ sim $ 1 km $^3 $）的水下中微子望远镜，安装在贝加尔湖的新鲜水域中。深湖水环境被背景光所弥漫，Baikal-GVD的光传感器可以检测到。我们引入了一个神经网络，以有效地将这些噪声命中与信号的命中分离，这是由于相对论颗粒通过检测器的传播而引起的。该模型具有类似U-NET的架构，并采用事件的时间（因果）结构。神经网络的指标可达到99 \％信号纯度（精度）和96 \％的生存效率（回忆），上面是蒙特卡罗模拟数据集。我们将开发的方法与算法方法进行了比较，以拒绝噪声并讨论神经网络（包括基于图基网络）的其他可能的架构。
Baikal-GVD is a large ($\sim$1 km$^3$) underwater neutrino telescope installed in the fresh waters of Lake Baikal. The deep lake water environment is pervaded by background light, which is detectable by Baikal-GVD's photosensors. We introduce a neural network for an efficient separation of these noise hits from the signal ones, stemming from the propagation of relativistic particles through the detector. The model has a U-net-like architecture and employs temporal (causal) structure of events. The neural network's metrics reach up to 99\% signal purity (precision) and 96\% survival efficiency (recall) on Monte-Carlo simulated dataset. We compare the developed method with the algorithmic approach to rejecting the noise and discuss other possible architectures of neural networks, including graph-based ones.
