The search for gravitational-wave (GW) signals is limited by non-Gaussian transient noises that mimic astrophysical signals. Temporal coincidence between two or more detectors is used to mitigate contamination by these instrumental glitches. However, when a single detector is in operation, coincidence is impossible, and other strategies have to be used. We explore the possibility of using neural network classifiers and present the results obtained with three types of architectures: convolutional neural network, temporal convolutional network, and inception time. The last two architectures are specifically designed to process time-series data. The classifiers are trained on a month of data from the LIGO Livingston detector during the first observing run (O1) to identify data segments that include the signature of a binary black hole merger. Their performances are assessed and compared. We then apply trained classifiers to the remaining three months of O1 data, focusing specifically on single-detector times. The most promising candidate from our search is 4 January 2016 12:24:17 UTC. Although we are not able to constrain the significance of this event to the level conventionally followed in GW searches, we show that the signal is compatible with the merger of two black holes with masses m1=50.7−8.9+10.4M⊙ and m2=24.4−9.3+20.2M⊙ at the luminosity distance of dL=564−338+812Mpc .

中文翻译：

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用于单探测器周期引力波搜索的神经网络时间序列分类器


对引力波（GW）信号的搜索受到模仿天体物理信号的非高斯瞬态噪声的限制。两个或多个探测器之间的时间重合用于减轻这些仪器故障造成的污染。然而，当单个探测器运行时，重合是不可能的，必须使用其他策略。我们探索使用神经网络分类器的可能性，并展示使用三种类型的架构获得的结果：卷积神经网络、时间卷积网络和起始时间。最后两种架构是专门为处理时间序列数据而设计的。在第一次观测运行 (O1) 期间，分类器接受了 LIGO 利文斯顿探测器一个月的数据训练，以识别包含双黑洞合并特征的数据段。他们的表现会被评估和比较。然后，我们将经过训练的分类器应用于剩余三个月的 O1 数据，特别关注单探测器时间。我们搜索中最有希望的候选时间是 2016 年 1 月 4 日 12:24:17 UTC。尽管我们无法将这一事件的重要性限制在引力波搜索中常规遵循的水平，但我们表明该信号与质量为 m1=50.7−8.9+10.4M⊙ 和 m2=24.4 的两个黑洞的合并兼容−9.3+20.2M⊙ 在光度距离 dL=564−338+812Mpc 处。