核心坍缩超新星引力波搜索和深度学习分类
Core-Collapse Supernova Gravitational-Wave Search and Deep Learning Classification
摘要：
我们描述了一个搜索和分类程序的核心塌陷超新星（CCSN）爆炸发出的引力波，使用卷积神经网络（CNN）结合事件触发器发生器被称为小波检测滤波器（WDF）。我们使用时间序列引力波数据作为输入的1-D CNN搜索，以及使用数据的时频表示作为输入的2-D CNN搜索。为了测试我们的1-D和2-D CNN分类的准确性，我们将来自中微子驱动的核心坍缩的最新流体动力学模拟的CCSN波形添加到使用Virgo干涉仪和计划中的爱因斯坦望远镜灵敏度曲线模拟的高斯色噪声中。我们发现，对于一个单一的检测器，1-D和2-D CNN管道的分类精度超过95%。在机器学习CCSN研究中，我们首次将短持续时间的检测器噪声瞬态添加到我们的数据中，以测试我们的方法对检测器噪声伪影产生的假警报的鲁棒性。此外，我们表明，CNN可以区分不同类型的CCSN波形模型。
英文摘要：
We describe a search and classification procedure for gravitational waves emitted by core-collapse supernova (CCSN) explosions, using a convolutional neural network (CNN) combined with an event trigger generator known as Wavelet Detection Filter (WDF). We employ both a 1-D CNN search using time series gravitational-wave data as input, and a 2-D CNN search with time-frequency representation of the data as input. To test the accuracies of our 1-D and 2-D CNN classification, we add CCSN waveforms from the most recent hydrodynamical simulations of neutrino-driven core-collapse to simulated Gaussian colored noise with the Virgo interferometer and the planned Einstein Telescope sensitivity curve. We find classification accuracies, for a single detector, of over 95% for both 1-D and 2-D CNN pipelines. For the first time in machine learning CCSN studies, we add short duration detector noise transients to our data to test the robustness of our method against false alarms created by detector noise artifacts. Further to this, we show that the CNN can distinguish between different types of CCSN waveform models.
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