论文标题
带有扩张的卷积自动编码器的二进制黑洞的deno降压引力波信号
Denoising gravitational-wave signals from binary black holes with dilated convolutional autoencoder
论文作者
论文摘要
重力波检测器的宽带频率输出是一个非平稳和非高斯时间序列数据流，该数据流由局部干扰和瞬时伪影群体主导，该噪声与引力波信号的相同时间表演变，并可能破坏天文学信息。我们研究了一种用于通过在编码器编码器配置中采用卷积神经网络来揭示天体物理信号的denoising算法，即在公开可用的Ligo O1时间序列序列数据中应用合并二进制黑洞信号的denoising程序。 Denoising卷积自动编码器神经网络在模拟的天体物理信号的数据集上进行了培训，该数据集注入了真实的检测器的噪声和检测器噪声伪影的数据集（“ Glitches”），其忠诚度在O1和O1和O2 Ligo-virgo的真实重力事件上进行了测试。
Broadband frequency output of gravitational-wave detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the gravitational-wave signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e. apply the denoising procedure of coalescing binary black hole signals in the publicly available LIGO O1 time series strain data. The denoising convolutional autoencoder neural network is trained on a dataset of simulated astrophysical signals injected into the real detector's noise and a dataset of detector noise artifacts ("glitches"), and its fidelity is tested on real gravitational-wave events from O1 and O2 LIGO-Virgo observing runs.
