论文标题
基于神经网络的Level-1触发升级，用于Snolab的SuperCDMS实验
Neural-network-based level-1 trigger upgrade for the SuperCDMS experiment at SNOLAB
论文作者
论文摘要
SuperCDMS Snolab暗物质搜索实验的扩展物理程序旨在最大程度地提高对低质量暗物质的敏感性。为了意识到这一点，提出了通过使用触发FPGA上实现的复发神经网络来升级数据采集系统的现有级别1触发器。这提供了改进的振幅估计器和信号噪声歧视器，基于单个检测器通道的过滤痕迹的组合信息。本文讨论了该神经触发器的结构和配置，并根据信号模拟和噪声数据来量化关键性能指标（例如效率，分辨率和噪声速率）的改进。根据此概念证明的发现，触发阈值预计将降低约22％。
The extended physics program of the SuperCDMS SNOLAB dark matter search experiment aims to maximize the sensitivity to low-mass dark matter. To realize this, an upgrade of the existing level-1 trigger of the data acquisition system is proposed by making use of a recurrent neural network to be implemented on the trigger FPGA. This provides an improved amplitude estimator and signal-noise discriminator based on the combined information of filtered traces from individual detector channels. The architecture and configuration of this neural trigger are discussed in this article, and the improvements in key performance indicators such as the efficiency, resolution, and noise rate are quantified based on signal simulations and noise data. Based on the findings in this proof of concept, the trigger threshold is expected to be lowered by ~22%.
