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

arXiv - PHYS - High Energy Physics - Experiment Pub Date : 2024-09-02 DOI:arxiv-2409.01026

Pablo Correafor the GRAND collaboration, Jean-Marc Colleyfor the GRAND collaboration, Tim Huegefor the GRAND collaboration, Kumiko Koterafor the GRAND collaboration, Sandra Le Cozfor the GRAND collaboration, Olivier Martineau-Huynhfor the GRAND collaboration, Markus Rothfor the GRAND collaboration, Xishui Tianfor the GRAND collaboration

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Abstract

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.

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为 GRAND 开发自主探测装置自触发器

中微子探测巨型射电阵列（GRAND）在对大范围气阵雨进行射电探测时遇到的主要挑战之一是需要自主的射电自触发。这项工作介绍了目前在中微子探测巨型射电阵列（GRAND）项目背景下，在探测单元层面上开发的自触发技术，即所谓的第一级触发（FLT）。阵列级的第二级触发（SLT）在另一篇论文中进行了描述。文中介绍了基于模板拟合算法和卷积神经网络（CNN）的两种 FLT 方法。在这项工作中，我们比较了两种 FLT 方法在信号选择效率和背景剔除效率方面的初步离线性能。我们发现，对于这两种方法，如果在 5 美元（sigma$）的水平上要求信号选择效率达到 90%，则可以剔除 40%的背景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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arXiv - PHYS - High Energy Physics - Experiment

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