论文标题
核心溢出超新星重力波搜索和深度学习分类
Core-Collapse Supernova Gravitational-Wave Search and Deep Learning Classification
论文作者
论文摘要
我们使用卷积神经网络（CNN）与事件触发器发生器（Wavelet检测过滤器（WDF））相结合，描述了由核心偏离超新星（CCSN）爆炸发出的重力波的搜索和分类程序。我们使用时间序列重力波数据作为输入采用1-D CNN搜索，以及以数据为输入的时间频表示的2-D CNN搜索。为了测试我们的1-D和2-D CNN分类的精确度，我们从中微子驱动的核心偏曲曲线的最新流体动力学模拟中添加了CCSN波形，以使用处女座干涉仪和计划中的爱因斯坦望远镜灵敏度曲线模拟高斯有色噪声。我们发现，对于1-D和2-D CNN管道，单个检测器的分类精度超过95％。在机器学习CCSN研究中，我们首次将较短的持续时间检测器噪声瞬变添加到我们的数据中，以测试我们方法对检测器噪声伪像引起的错误警报的鲁棒性。除此之外，我们表明CNN可以区分不同类型的CCSN波形模型。
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.
