论文标题
超级分辨率卷积神经网络用于光谱数据中的特征提取
Super Resolution Convolutional Neural Network for Feature Extraction in Spectroscopic Data
论文作者
论文摘要
二维（2D）峰值发现是物理实验数据分析的常见实践，通常通过计算局部衍生物来实现。但是，当局部景观复杂或数据的信噪比较低时，此方法本质上是不稳定的。在这项工作中，我们提出了一种新方法，其中峰跟踪任务被形式化为反问题，因此可以通过卷积神经网络（CNN）解决。此外，我们表明实验的基本物理原理可用于生成训练数据。通过将受过训练的神经网络推广到实际实验数据上，我们表明，与传统基于衍生的方法相比，CNN方法可以实现可比或更好的结果。当已知物理过程时，可以在不同的物理实验中进一步概括这种方法。
Two dimensional (2D) peak finding is a common practice in data analysis for physics experiments, which is typically achieved by computing the local derivatives. However, this method is inherently unstable when the local landscape is complicated, or the signal-to-noise ratio of the data is low. In this work, we propose a new method in which the peak tracking task is formalized as an inverse problem, thus can be solved with a convolutional neural network (CNN). In addition, we show that the underlying physics principle of the experiments can be used to generate the training data. By generalizing the trained neural network on real experimental data, we show that the CNN method can achieve comparable or better results than traditional derivative based methods. This approach can be further generalized in different physics experiments when the physical process is known.
