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arXiv 2105.13664gr-qc
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用卷积神经网络检测引力波信号的早期螺旋

Detecting the early inspiral of a gravitational-wave signal with convolutional neural networks

Grégory Baltus, Justin Janquart, Melissa Lopez, Amit Reza, Sarah Caudill, Jean-René Cudell

摘要:

介绍了一种新的引力波预警系统的运行方法。它基于短卷积神经网络。我们专注于紧凑的二元合并,轻,中,重的二元中子星系统。这些信号是一维时间序列$-$,即注入高斯噪声中的白化时间应变$-$,该高斯噪声是从LIGO探测器的功率谱密度以设计灵敏度构建的。我们构建了短的一维卷积神经网络,通过在早期螺旋的一部分上训练它们来检测这些类型的事件。我们表明,这样的网络能够从波形的一小部分中检索这些信号。

英文摘要:

We introduce a novel methodology for the operation of an early %warning alert system for gravitational waves. It is based on short convolutional neural networks. We focus on compact binary coalescences, for light, intermediate and heavy binary-neutron-star systems. The signals are 1-dimensional time series $-$ the whitened time-strain $-$ injected in Gaussian noise built from the power-spectral density of the LIGO detectors at design sensitivity. We build short 1-dimensional convolutional neural networks to detect these types of events by training them on part of the early inspiral. We show that such networks are able to retrieve these signals from a small portion of the waveform.

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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