Deep-learning continuous gravitational waves
文献类型：期刊论文
| 作者 | Dreissigacker, Christoph1,2; Sharma, Rahul1,2,3; Messenger, Chris4; Zhao, Ruining5,6,7; Prix, Reinhard1,2 |
| 刊名 | PHYSICAL REVIEW D
 |
| 出版日期 | 2019-08-07 |
| 卷号 | 100期号:4页码:11 |
| ISSN号 | 2470-0010 |
| DOI | 10.1103/PhysRevD.100.044009 |
| 英文摘要 | We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals [D. George and E. A. Huerta, Phys. Rev. D 97, 044039 (2018); H. Gabbard, M. Williams, F. Hayes, and C. Messenger, Phys. Rev. Lett. 120, 141103 (2018)]. We train a convolutional neural network with residual (shortcut) connections and compare its detection power to that of a fully coherent matched-filtering search using the WEAVE pipeline [K. Wette, S. Walsh, R. Prix, and M. A. Papa, Phys. Rev. D 97, 123016 (2018)]. As test benchmarks we consider two types of all-sky searches over the frequency range from 20 to 1000 Hz: an "easy" search using T = 10(5) s of data, and a "harder" search using T = 10(6) s. The detection probability p(det) is measured on a signal population for which matched filtering achieves P-det = 90% in Gaussian noise. In the easiest test case (T = 10(5) s at 20 Hz) the DNN achieves p(det) similar to 88%, corresponding to a loss in sensitivity depth of similar to 5% versus coherent matched filtering. However, at higher frequencies and for longer observation times the DNN detection power decreases, until p(det )similar to 13% and a loss of similar to 66% in sensitivity depth in the hardest case (T = 10(6) s at 1000 Hz). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search. |
| 资助项目 | Science and Technology Research Council[ST/L000946/1] ; European Cooperation in Science and Technology (COST) action[CA17137] |
| WOS研究方向 | Astronomy & Astrophysics ; Physics |
| 语种 | 英语 |
| 出版者 | AMER PHYSICAL SOC |
| WOS记录号 | WOS:000479032600004 |
| 资助机构 | Science and Technology Research Council ; Science and Technology Research Council ; European Cooperation in Science and Technology (COST) action ; European Cooperation in Science and Technology (COST) action ; Science and Technology Research Council ; Science and Technology Research Council ; European Cooperation in Science and Technology (COST) action ; European Cooperation in Science and Technology (COST) action ; Science and Technology Research Council ; Science and Technology Research Council ; European Cooperation in Science and Technology (COST) action ; European Cooperation in Science and Technology (COST) action ; Science and Technology Research Council ; Science and Technology Research Council ; European Cooperation in Science and Technology (COST) action ; European Cooperation in Science and Technology (COST) action |
| 源URL | [http://ir.bao.ac.cn/handle/114a11/27348]  |
| 专题 | 中国科学院国家天文台 |
| 通讯作者 | Dreissigacker, Christoph |
| 作者单位 | 1.Albert Einstein Inst, Max Planck Inst Gravitat Phys, D-30167 Hannover, Germany 2.Leibniz Univ Hannover, D-30167 Hannover, Germany 3.Birla Inst Technol & Sci, Pilani 333031, Rajasthan, India 4.Univ Glasgow, Sch Phys & Astron, SUPA, Glasgow G12 8QQ, Lanark, Scotland 5.Chinese Acad Sci, Natl Astron Observ, Key Lab Opt Astron, Beijing 100101, Peoples R China 6.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 7.Beijing Normal Univ, Dept Astron, Beijing 100875, Peoples R China |
| 推荐引用方式 GB/T 7714 | Dreissigacker, Christoph,Sharma, Rahul,Messenger, Chris,et al. Deep-learning continuous gravitational waves[J]. PHYSICAL REVIEW D,2019,100(4):11. |
| APA | Dreissigacker, Christoph,Sharma, Rahul,Messenger, Chris,Zhao, Ruining,&Prix, Reinhard.(2019).Deep-learning continuous gravitational waves.PHYSICAL REVIEW D,100(4),11. |
| MLA | Dreissigacker, Christoph,et al."Deep-learning continuous gravitational waves".PHYSICAL REVIEW D 100.4(2019):11. |
入库方式： OAI收割
来源：国家天文台
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