论文标题
卷积神经网络，以区分小故障和长时间的重力波瞬变
A convolutional neural network to distinguish glitches from minute-long gravitational wave transients
论文作者
论文摘要
引力波爆发是瞬时信号，与紧凑型二元合并不同，这些二进制合并是由各种天体物理现象引起的。由于这些现象大多数的建模很差，因此排除了传统搜索方法（例如匹配的过滤）的使用。爆发包括短（$ <$ 10秒）和长度（从10到几百秒）持续时间信号，检测受到环境和仪器瞬态噪声的约束，称为故障。小故障污染爆发搜索，减少了有用的数据量并限制了当前算法的灵敏度。因此，将它们与潜在的爆发信号区分开是原始的重要性。在本文中，我们建议训练卷积神经网络，以检测跨相关的Ligo噪声的时频空间中的故障。我们表明，我们的网络正在检索超过95美元的故障，同时仅在现有的小故障类的子集中接受培训，以突出网络对全新的小故障类的敏感性。
Gravitational wave bursts are transient signals distinct from compact binary mergers that arise from a wide variety of astrophysical phenomena. Because most of these phenomena are poorly modeled, the use of traditional search methods such as matched filtering is excluded. Bursts include short ($<$10 seconds) and long (from 10 to a few hundreds of seconds) duration signals for which the detection is constrained by environmental and instrumental transient noises called glitches. Glitches contaminate burst searches, reducing the amount of useful data and limiting the sensitivity of current algorithms. It is therefore of primordial importance to locate and distinguish them from potential burst signals. In this paper, we propose to train a convolutional neural network to detect glitches in the time-frequency space of the cross-correlated LIGO noise. We show that our network is retrieving more than 95$\%$ of the glitches while being trained only on a subset of the existing glitch classes highlighting the sensitivity of the network to completely new glitch classes.
