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Alberto Regadío¹, J Ignacio García Tejedor¹, Luis Esteban²

¹Space Research Group, Universidad de Alcalá, 28805 Alcalá de Henares, Spain.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine

|December 2, 2023

概括

神经网络 (NN) 为粒子探测器提供先进的脉冲塑造,改善信号噪声比 (SNR). 卷积神经网络 (CNN) 在高频,白噪声环境中表现出卓越的性能.

科学领域:

粒子物理仪器仪器仪表粒子物理仪器仪表
信号处理信号处理
机器学习应用程序机器学习应用程序

背景情况:

在粒子探测器中,优化信号噪声比 (SNR) 是至关重要的.
线性脉冲成型方法是标准的,但有局限性.
非线性方法,如神经网络 (NN),显示了提高性能的潜力.

研究的目的:

为了研究脉冲塑造的三个不同的神经网络 (NN) 架构.
为了比较卷积神经网络 (CNN),循环神经网络 (RNN) 和自我减弱的神经网络的性能.
评估NNs处理脉冲展开,和和噪声的能力.

主要方法:

开发和应用三个NN架构:CNN,RNN和自我减弱的NN.
使用受布朗和白噪声影响的CR-RC脉冲进行测试.
评估脉冲塑造,展开和和恢复能力.

主要成果:

所有呈现的NN拓都有效地塑造了脉冲,避免了堆叠.
在高频白噪声条件下,CNN表现优于RNN和自我减弱的NNN.
所有三个NN架构都在布朗噪声下产生类似的结果,无论噪声水平如何.

结论:

神经网络为粒子探测器提供了强大的非线性替代传统线性造型器.
在高到达频率和白噪声的挑战性条件下,CNN对于脉冲塑造特别有效.
提出的NN方法可以适应各种脉冲形状和噪声类型.

关键词:

数字脉冲处理数字脉冲处理仪器化仪器化仪器化神经网络的神经网络的神经网络

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