论文标题
在Ligo/处女座第二观察期间使用神经网络搜索紧凑型二元合并事件
Searches for Compact Binary Coalescence Events using Neural Networks in LIGO/Virgo Second Observation Period
论文作者
论文摘要
我们介绍了使用卷积神经网络和Ligo/Pirgo数据搜索紧凑型二进制合并的结果，与O2观察期相对应。时间和频率中的二维图像用作输入，两组神经网络分别训练低质量（0.2-2.0 msun）和高质量（25-100 msun）紧凑型二元合并事件。我们探索了来自单个或一对干涉仪的输入信息训练的神经网络，这表明来自成对的信息的使用会导致性能提高。使用卷积神经网络对完整的O2数据集进行检测的扫描表明，使用匹配的过滤技术，性能与规范管道的性能兼容。在O2数据中没有发现具有显着信噪比的其他事件。
We present results on the search for the coalescence of compact binary mergers using convolutional neural networks and the LIGO/Virgo data, corresponding to the O2 observation period. Two-dimensional images in time and frequency are used as input, and two sets of neural networks are trained separately for low mass (0.2 - 2.0 Msun) and high mass (25 - 100 Msun) compact binary coalescence events. We explored neural networks trained with input information from a single or a pair of interferometers, indicating that the use of information from pairs leads to an improved performance. A scan over the full O2 data set using the convolutional neural networks for detection demonstrates that the performance is compatible with that from canonical pipelines using matched filtering techniques. No additional events with significant signal-to-noise ratio are found in the O2 data.
