Bibliographic Details
| Title: |
Space-based gravitational wave signal detection and extraction with deep neural network |
| Authors: |
Tianyu Zhao, Ruoxi Lyu, He Wang, Zhoujian Cao, Zhixiang Ren |
| Source: |
Communications Physics, Vol 6, Iss 1, Pp 1-12 (2023) |
| Publisher Information: |
Nature Portfolio, 2023. |
| Publication Year: |
2023 |
| Collection: |
LCC:Astrophysics
LCC:Physics |
| Subject Terms: |
Astrophysics, QB460-466, Physics, QC1-999 |
| More Details: |
Abstract Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will require a complex template bank, leading to a computational cost that is too expensive in practice. Here, we develop a high-accuracy GW signal detection and extraction method for all space-based GW sources. As a proof of concept, we show that a science-driven and uniform multi-stage self-attention-based deep neural network can identify synthetic signals that are submerged in Gaussian noise. Our method exhibits a detection rate exceeding 99% in identifying signals from various sources, with the signal-to-noise ratio at 50, at a false alarm rate of 1%. while obtaining at least 95% similarity compared with target signals. We further demonstrate the interpretability and strong generalization behavior for several extended scenarios. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2399-3650 |
| Relation: |
https://doaj.org/toc/2399-3650 |
| DOI: |
10.1038/s42005-023-01334-6 |
| Access URL: |
https://doaj.org/article/743e494077be44419b694b2212bb5da9 |
| Accession Number: |
edsdoj.743e494077be44419b694b2212bb5da9 |
| Database: |
Directory of Open Access Journals |
