论文标题
用于长时间爆发搜索的机器学习算法
A machine learning algorithm for minute-long Burst searches
论文作者
论文摘要
长达一分钟的重力波（GW）瞬变是持续时间从几百秒钟持续的事件。为了与紧凑的二进制合并相反，它们的GW信号涵盖了广泛理解的天体物理现象，例如积聚磁盘的不稳定性和磁性耀斑。缺乏准确且快速生成的重力波发射模型阻止了使用匹配的滤波方法。因此，通过无模板的多余功率方法探测了此类事件，包括在检测器之间寻找相关的时频空间中的局部过量功率。该问题可以看作是在图像中寻找高价值聚类像素的搜索，通常通过深度学习算法（例如卷积神经网络（CNN））来解决该问题。在这项工作中，我们将CNN用作长期搜索的异常检测工具。我们表明，尽管经过最少的假设训练，但它可以达到像素的检测，同时能够从检测器内的仪器耦合中检索天体物理信号和噪声瞬变。我们还注意到，我们的神经网络可以推断并连接时间频平面中的部分不相交信号轨道。
Minute-long Gravitational Wave (GW) transients are events lasting from few to hundreds of seconds. In opposition to compact binary mergers, their GW signals cover a wide range of poorly understood astrophysical phenomena such as accretion disk instabilities and magnetar flares. The lack of accurate and rapidly generated gravitational-wave emission models prevents the use of matched filtering methods. Such events are thus probed through the template-free excess-power method, consisting in searching for a local excess of power in the time-frequency space correlated between detectors. The problem can be viewed as a search for high-value clustered pixels within an image, which has been generally tackled by deep learning algorithms such as Convolutional Neural Networks (CNNs). In this work, we use a CNN as a anomaly detection tool for the long-duration searches. We show that it can reach a pixel-wise detection despite trained with minimal assumptions, while being able to retrieve both astrophysical signals and noise transients originating from instrumental coupling within the detectors. We also note that our neural network can extrapolate and connect partially disjoint signal tracks in the time-frequency plane.
