标题： GRAND自主探测单元自触发装置的发展 显示英文标题

标题： Development of an Autonomous Detection-Unit Self-Trigger for GRAND

摘要： GRAND（广延大气簇射的巨型无线电阵列）所面临的广泛空气簇射无线电探测的主要挑战之一是需要一种自主的无线电自触发技术。 本文介绍了NUTRIG项目背景下检测单元级自触发技术（即所谓的第一级触发器，FLT）的当前发展情况。 阵列级的第二级触发器（SLT）将在另一篇贡献中描述。 文中描述了两种FLT方法，基于模板拟合算法和卷积神经网络（CNN）。 在这项工作中，我们比较了这两种FLT方法在信号选择效率和背景拒绝效率方面的初步离线性能。 我们发现，如果要求在${\gtrsim}40\%$水准上达到90%的信号选择效率，则两种方法都能拒绝$5\sigma$的背景。 显示英文摘要

摘要： One of the major challenges for the radio detection of extensive air showers, as encountered by the Giant Radio Array for Neutrino Detection (GRAND), is the requirement of an autonomous radio self-trigger. This work presents the current development of self-triggering techniques at the detection-unit level -- the so-called first-level trigger (FLT) -- in the context of the NUTRIG project. A second-level trigger (SLT) at the array level is described in a separate contribution. Two FLT methods are described, based on a template-fitting algorithm and a convolutional neural network (CNN). In this work, we compare the preliminary offline performance of both FLT methods in terms of signal selection efficiency and background rejection efficiency. We find that for both methods, ${\gtrsim}40\%$ of the background can be rejected if a signal selection efficiency of 90\% is required at the $5\sigma$ level.