Speaker
Jianqin Xu
Description
Generative Adversarial Networks (GANs), as a powerful framework in deep learning, have been widely applied in various fields. This study aims to develop a rapid data generator by training GAN networks on the data collected from the Pandax-II Run11 AmBe experiment. The data generated by the GAN network not only preserves the individual physical characteristics but also maintains the correlations between different physical quantities. During the network training process, we determine the optimal model through threshold searching and quantify the uncertainty in the training process to demonstrate that the generated data effectively represents the original dataset
