论文标题
卷积自动编码器用于clas12的漂移室数据删除
Convolutional Auto-Encoders for Drift Chamber data de-noising for CLAS12
论文作者
论文摘要
在本文中，我们介绍了使用卷积自动编码器来消除CLAS12漂移室的原始数据的结果。去噪声神经网络可提高轨道重建效率，并提高了高光度实验数据收集的性能。与先前开发的轨道分类器神经网络结合使用的去命名神经网络\ cite {gavalian：2022hfa}导致当前发光度的轨道重建效率显着提高（$ 0.6 \ times10^{35} {35} 〜cm^cm^cm^{ - 2} 〜sec^sec^sec^sec^sec^{ - 1} $）。实验测量数量的增加将使以相同的轨道重建效率的光度两倍以光度进行运行实验。这将导致加速器的运营成本巨大节省，并为杰斐逊实验室和合作机构节省大量资金。
In this article, we present the results of using Convolutional Auto-Encoders for de-noising raw data for CLAS12 drift chambers. The de-noising neural network provides increased efficiency in track reconstruction and also improved performance for high luminosity experimental data collection. The de-noising neural network used in conjunction with the previously developed track classifier neural network \cite{Gavalian:2022hfa} lead to a significant track reconstruction efficiency increase for current luminosity ($0.6\times10^{35}~cm^{-2}~sec^{-1}$ ). The increase in experimentally measured quantities will allow running experiments at twice the luminosity with the same track reconstruction efficiency. This will lead to huge savings in accelerator operational costs, and large savings for Jefferson Lab and collaborating institutions.
