| Project/Area Number |
17K05437
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Particle/Nuclear/Cosmic ray/Astro physics
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| Research Institution | Nagaoka University of Technology |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
秋月 拓磨 豊橋技術科学大学, 工学(系)研究科(研究院), 助教 (40632922)
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| Project Period (FY) |
2017-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2019: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥2,210,000 (Direct Cost: ¥1,700,000、Indirect Cost: ¥510,000)
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| Keywords | 重力波物理学・天文学 / 宇宙物理学 / 重力波データ解析 / 時間-周波数解析 / 機械学習 / 重力波天文学 |
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| Outline of Final Research Achievements |
The algorithm based on Hilbert-Huang transform (HHT) analysis and machine learning has been developed as a method for time series analysis of nonlinear and nonstationary data, and it enables us to perform a high resolution time frequency analysis of signals with strong frequency modulation by evaluating the instantaneous variation of amplitude and frequency of data.
By using the developed method, we analyzed gravitational waves from black hole quasi normal mode and core collapse supernova, respectively. Moreover, the noise selection and noise reduction method based on machine learning was proposed.
We suggested that the analysis method based on the HHT and machine learning was also effective for gravitational wave analysis, and indicated the necessity of further research.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究で開発を進めた重力波解析手法は、適応時間周波数解析と機械学習を基盤としている。近年、機械学習やAIなどの情報技術は急速に発展し、重力波データ分析にこれらの最新の方法を適用する価値がある。
一方で、開発した適応型の信号処理と機械学習は、音声処理、画像処理、生体信号処理（心電図、筋電、EEGを含む）やスポーツなどの幅広い分野において、潜在的なアプリケーションがあると考えられる。そのため、本研究によって得られた知識は重力波データ解析の枠を超えて広範囲に影響を与える可能性がある。
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