arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.03745astro-ph.HEgr-qc
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用机器学习增强引力波科学

Enhancing Gravitational-Wave Science with Machine Learning

Elena Cuoco, Jade Powell, Marco Cavaglià, Kendall Ackley, Michal Bejger, Chayan Chatterjee, Michael Coughlin, Scott Coughlin, Paul Easter, Reed Essick, Hunter G… 展开作者

Elena Cuoco, Jade Powell, Marco Cavaglià, Kendall Ackley, Michal Bejger, Chayan Chatterjee, Michael Coughlin, Scott Coughlin, Paul Easter, Reed Essick, Hunter Gabbard, Timothy Gebhard, Shaon Ghosh, Leila Haegel, Alberto Iess, David Keitel, Zsuzsa Marka, Szabolcs Marka, Filip Morawski, Tri Nguyen, Rich Ormiston, Michael Puerrer, Massimiliano Razzano, Kai Staats, Gabriele Vajente, Daniel Williams

摘要:

机器学习已经成为解决天体物理学问题的一种流行而强大的方法。我们回顾了机器学习技术在地基引力波探测器数据分析中的应用。例子包括提高高级LIGO和高级Virgo引力波搜索灵敏度的技术,快速测量引力波源天体物理参数的方法,以及减少和表征非天体物理探测器噪声的算法。这些应用展示了如何利用机器学习技术来增强当前和未来引力波探测器的科学性。

英文摘要:

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 detector data. Examples include techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo gravitational-wave searches, methods for fast measurements of the astrophysical parameters of gravitational-wave 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 gravitational-wave detectors.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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