Gravitational wave signal extraction against non-stationary instrumental noises with deep neural network
文献类型：期刊论文
| 作者 | Xu YX(许宇翔)4,5,6,7; Du MH(杜明辉)6; Xu P(徐鹏)3,4,6,7; Liang, Bo4,5,6,7; Wang, He1,2,4 |
| 刊名 | PHYSICS LETTERS B
 |
| 出版日期 | 2024-11-01 |
| 卷号 | 858页码:10 |
| ISSN号 | 0370-2693 |
| DOI | 10.1016/j.physletb.2024.139016 |
| 通讯作者 | Xu, Peng(xupeng@imech.ac.cn) |
| 英文摘要 | Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission (Taiji and Tianqin), will offer novel perspectives for exploring our Universe while introduce new challenges, especially in data analysis. Aside from the known challenges like high parameter space dimension, superposition of large number of signals etc., gravitational wave detections in space would be more seriously affected by anomalies or non-stationarities in the science measurements. Considering the three types of foreseeable non-stationarities including data gaps, transients (glitches), and time-varying noise auto-correlations, which may come from routine maintenance or unexpected disturbances during science operations, we developed a deep learning model for accurate signal extractions confronted with such anomalous scenarios. Our model exhibits the same performance as the current state-of-the-art models do for the ideal and anomaly free scenario, while shows remarkable adaptability in extractions of coalescing massive black hole binary signal against all three types of non-stationarities and even their mixtures. This also provide new explorations into the robustness studies of deep learning models for data processing in space-borne gravitational wave missions. |
| 分类号 | 一类 |
| WOS关键词 | DENOISING-AUTOENCODER ; SPEECH |
| 资助项目 | National Key Research and Development Program of China[2021YFC2201901] ; National Key Research and Development Program of China[2021YFC2201903] ; National Key Research and Development Program of China[2020YFC2200601] ; National Key Research and Development Program of China[2020YFC2200901] |
| WOS研究方向 | Astronomy & Astrophysics ; Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001317639000001 |
| 资助机构 | National Key Research and Development Program of China |
| 其他责任者 | Xu, Peng |
| 源URL | [http://dspace.imech.ac.cn/handle/311007/96718]  |
| 专题 | 力学研究所_国家微重力实验室 |
| 作者单位 | 1.Univ Chinese Acad Sci, Int Ctr Theoret Phys Asia Pacific, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Theoret Phys, CAS Key Lab Theoret Phys, Beijing 100190, Peoples R China; 3.Lanzhou Univ, Lanzhou Ctr Theoret Phys, Lanzhou 730000, Peoples R China; 4.Univ Chinese Acad Sci UCAS, Taiji Lab Gravitat Wave Universe Beijing Hangzhou, Beijing 100049, Peoples R China; 5.Chinese Acad Sci, Shanghai Inst Opt & Fine Mech, Shanghai 201800, Peoples R China; 6.Chinese Acad Sci, Inst Mech, Ctr Gravitat Wave Expt, Natl Micrograv Lab, Beijing 100190, Peoples R China; 7.UCAS, Hangzhou Inst Adv Study, Hangzhou 310024, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Xu YX,Du MH,Xu P,et al. Gravitational wave signal extraction against non-stationary instrumental noises with deep neural network[J]. PHYSICS LETTERS B,2024,858:10. |
| APA | 许宇翔,杜明辉,徐鹏,Liang, Bo,&Wang, He.(2024).Gravitational wave signal extraction against non-stationary instrumental noises with deep neural network.PHYSICS LETTERS B,858,10. |
| MLA | 许宇翔,et al."Gravitational wave signal extraction against non-stationary instrumental noises with deep neural network".PHYSICS LETTERS B 858(2024):10. |
入库方式： OAI收割
来源：力学研究所
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