  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.
