Matched-filtering Techniques and Deep Neural Networks for Gravitational Wave Astronomy
Channel: Apache MXNet
Date: 2021-02-08

He Wang, Postdoc @ ITP-CAS, Ph.D. from BNU.

Deep learning is a neural-inspired pattern recognition technique that has been shown to be as effective as conventional signal processing. And It has been shown have considerable potential to identify gravitational-wave (GW) signals in highly noisy data. In this talk, I will first review some related works on the detection and characterization of GW signals and some fundamental background of GW data. I will then present our recent paper (DOI: 10.1103/physrevd.101.104003 https://journals.aps.org/prd/abstract/10.1103/PhysRevD.101.104003) about the effect of matched-filtering convolutional neural networks (MFCNN) we proposed on the GW recognition and identifying generalization properties of gravitational waves. Powered by MXNet, a brand-new network architecture is presented. At last, some insights on the model are presented.