Generalised gravitational wave burst generation with generative adversarial networks
Generalised gravitational wave burst generation with generative adversarial networks
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DOI：
10.1088/1361-6382/ac09cc
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发表时间：
2021-03
影响因子：
3.5
通讯作者：
J. McGinn;C. Messenger;M. J. Williams;I. Heng
中科院分区：
文献类型：
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作者：
J. McGinn;C. Messenger;M. J. Williams;I. Heng
我们介绍了条件生成对抗网络(CGAN)在时间域中用于广义引力波(GW)爆发的生成。生成性对抗网络是一种生成性机器学习模型，它根据训练数据集的特征产生新的数据。我们以五类经常用于描述GW突发搜索的时间序列信号为网络条件：正弦-高斯、振铃、白噪声突发、高斯脉冲和二进制黑洞合并。我们证明了该模型可以复制这些标准信号类的特征，并通过内插和类混合来产生广义突发信号。我们还给出了一个示例应用程序，其中卷积神经网络(CNN)分类器针对我们的CGAN生成的突发信号进行训练。我们表明，只对标准的五个信号类别训练的CNN分类器的检测效率低于对从组合信号类别空间中提取的一组广义突发信号训练的CNN分类器。
We introduce the use of conditional generative adversarial networks (CGANs) for generalised gravitational wave (GW) burst generation in the time domain. Generative adversarial networks are generative machine learning models that produce new data based on the features of the training data set. We condition the network on five classes of time-series signals that are often used to characterise GW burst searches: sine-Gaussian, ringdown, white noise burst, Gaussian pulse and binary black hole merger. We show that the model can replicate the features of these standard signal classes and, in addition, produce generalised burst signals through interpolation and class mixing. We also present an example application where a convolutional neural network (CNN) classifier is trained on burst signals generated by our CGAN. We show that a CNN classifier trained only on the standard five signal classes has a poorer detection efficiency than a CNN classifier trained on a population of generalised burst signals drawn from the combined signal class space.