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Enhancing gravitational-wave science with machine learning
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AI
DOI：10.1088/2632-2153/abb93a.png)
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摘要
En 中文
Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of ground-based gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors.
Keyword:
gravitational waves
machine learning
deep learning
期刊
M
IF:
4.6
论文数:
1.1K
被引数:
3.4K
机构
C
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引用论文
Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data高级引力波探测器II中噪声瞬变的分类方法: 高级LIGO数据的性能测试
OAAI
OAAI分享收藏Deep Learning for real-time gravitational wave detection and parameter estimation: Results with Advanced LIGO data用于实时引力波检测和参数估计的深度学习: 高级LIGO数据的结果.png)
PHYSICS LETTERS BOAAI
PHYSICS LETTERS BIF4.5
OAAI分享收藏Classification methods for noise transients in advanced gravitational-wave detectors高级引力波探测器中噪声瞬变的分类方法OAAI
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