论文标题
搜索使用Ligo/处女座第三观察期的神经网络搜索质量 - 空气紧凑的二元聚结合事件
Searches for Mass-Asymmetric Compact Binary Coalescence Events using Neural Networks in the LIGO/Virgo Third Observation Period
论文作者
论文摘要
我们介绍了使用卷积神经网络和O3观察期间使用卷积神经网络和LIGO/处女座数据的紧凑型二进制合并合并的结果。时间和频率中的二维图像用作输入。考虑到0.01 msun和20 msun的范围。我们探索了从单个干涉仪，一对干涉仪或所有三个干涉仪的对输入信息进行训练的神经网络，这表明使用最大信息可用的最大信息会导致性能提高。使用卷积神经网络对O3数据集进行扫描，从而从唯一的噪声假设中没有显着过多。结果被转化为合并率的90％置信度上限，这是二进制系统质量参数的函数。
We present the results on the search for the coalescence of compact binary mergers with very asymmetric mass configurations using convolutional neural networks and the LIGO/Virgo data for the O3 observation period. Two-dimensional images in time and frequency are used as input. Masses in the range between 0.01 Msun and 20 Msun are considered. We explore neural networks trained with input information from a single interferometer, pairs of interferometers, or all three interferometers together, indicating that the use of the maximum information available leads to an improved performance. A scan over the O3 data set using the convolutional neural networks for detection results into no significant excess from an only-noise hypothesis. The results are translated into 90% confidence level upper limits on the merger rate as a function of the mass parameters of the binary system.
