论文标题
基于主动目标时间投影室的卷积神经网络的数字信号分析
Digital Signal Analysis based on Convolutional Neural Networks for Active Target Time Projection Chambers
论文作者
论文摘要
在这项工作中开发了使用卷积神经网络（CNN）的数字信号分析算法。该算法的主要目的是对具有主动目标时间投影室的实验进行分析。该代码分为三个步骤：基线校正，信号反向卷积和峰检测和集成。 CNN能够学习具有小于6 \％的相对误差的信号处理模型。基于CNN的分析提供了与传统的反卷积算法相同的结果，但在计算时间方面（快65倍）方面效率更高。这为改进现有代码的新可能性开辟了新的可能性，并简化了主动目标实验中产生的大量数据的分析。
An algorithm for digital signal analysis using convolutional neural networks (CNN) was developed in this work. The main objective of this algorithm is to make the analysis of experiments with active target time projection chambers more efficient. The code is divided in three steps: baseline correction, signal deconvolution and peak detection and integration. The CNNs were able to learn the signal processing models with relative errors of less than 6\%. The analysis based on CNNs provides the same results as the traditional deconvolution algorithms, but considerably more efficient in terms of computing time (about 65 times faster). This opens up new possibilities to improve existing codes and to simplify the analysis of the large amount of data produced in active target experiments.
