Detecting gravitational waves from extreme mass ratio inspirals using convolutional neural networks

Zhang, X.-T., Messenger, C. , Korsakova, N., Chan, M. L., Hu, Y.-M. and Zhang, J.-d. (2022) Detecting gravitational waves from extreme mass ratio inspirals using convolutional neural networks. Physical Review D, 105(12), 123027. (doi: 10.1103/PhysRevD.105.123027)

Abstract

Extreme mass ratio inspirals (EMRIs) are among the most interesting gravitational wave (GW) sources for space-borne GW detectors. However, successful GW data analysis remains challenging due to many issues, ranging from the difficulty of modeling accurate waveforms, to the impractically large template bank required by the traditional matched filtering search method. In this work, we introduce a proof-of-principle approach for EMRI detection based on convolutional neural networks (CNNs). We demonstrate the performance with simulated EMRI signals buried in Gaussian noise. We show that over a wide range of physical parameters, the network is effective for EMRI systems with a signal-to-noise ratio larger than 50, and the performance is most strongly related to the signal-to-noise ratio. The method also shows good generalization ability toward different waveform models. Our study reveals the potential applicability of machine learning technology like CNNs toward more realistic EMRI data analysis.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Messenger, Dr Christopher
• Authors: Zhang, X.-T., Messenger, C., Korsakova, N., Chan, M. L., Hu, Y.-M., and Zhang, J.-d.
• Subjects: Q Science > QB Astronomy; Q Science > QC Physics
• College/School: College of Science and Engineering > School of Physics and Astronomy
• Research Centre: College of Science and Engineering > School of Physics and Astronomy > Institute for Gravitational Research
• Journal Name: Physical Review D
• Publisher: American Physical Society
• ISSN: 1550-7998
• ISSN (Online): 1550-2368
• Published Online: 24 June 2022
• Copyright Holders: Copyright © 2022 American Physical Society
• First Published: First published in Physical Review D 105(12):123027
• Publisher Policy: Reproduced in accordance with the copyright policy of the publisher

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
312546		Investigations in Gravitational Radiation	Sheila Rowan	Science and Technology Facilities Council (STFC)	ST/V005634/1	ENG - Electronics & Nanoscale Engineering
301541		Gravitational-wave Excellence through Alliance Training (GrEAT) Network with China	Ik Siong Heng	Science and Technology Facilities Council (STFC)	ST/R002770/1	P&S - Physics & Astronomy