I am currenly a postdoctoral researcher at the Institute of Theoretical Physics, Chinese Academy of Sciences. My research interests mainly focus on the study of theoretical modelling and the application of AI-based machine learning techniques to gravitational wave data analysis. In addition, I am also committed to related researches such as data processing methods and the development of algorithms for ground-based and space-based gravitational wave detection.
My doctoral dissertation, “Research on Data Analysis of Deep Learning in Gravitational Wave Detection”, discusses the difficulties and challenges of real-time detection and data processing of gravitational wave signals by ground-based gravitational wave detectors, and combines real gravitational wave data with traditional signal processing algorithms via deep neural network model to search for possible weak gravitational wave signals.
Ph.D. in Theoretical Physics, 2020
Beijing Normal University
M.Sc. in Theoretical Physics, 2015
China West Normal University
B.Sc. in Applied Physics, 2012
Chongqing Technology and Business University
Deep learning method develops very fast as a tool for data analysis these years. Such a technique is quite promising to treat gravitational wave detection data. There are many works already in the literature which used deep learning technique to process simulated gravitational wave data. In this paper we apply deep learning to LIGO O1 data. In order to improve the weak signal recognition we adjust the convolutional neural network (CNN) a little bit. Our adjusted convolutional neural network admits comparable accuracy and efficiency of signal recognition as other deep learning works published in the literature. Based on our adjusted CNN, we can clearly recognize the eleven confirmed gravitational wave events included in O1 and O2. And more we find about 2000 gravitational wave triggers in O1 data.