arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2004.01050gr-qcastro-ph.IM
收藏

从频域中的引力波信号进行深度学习合并质量估计

Deep learning merger masses estimation from gravitational waves signals in the frequency domain

Juan Pablo Marulanda, Camilo Santa, Antonio Enea Romano

摘要:

从致密双星合并中探测到引力波(GW)为研究多信使天体物理学提供了一个新的窗口。确定合并器参数的标准技术是匹配滤波,包括将信号与模板库进行比较。由于需要分析大量的实验数据,这种方法可能是耗时和计算昂贵的。在试图找到更有效的数据分析方法,我们开发了一个新的频域卷积神经网络(FCNN)预测合并质量的检测器信号的频谱图,并比较它的时域神经网络(TCNN)。由于FCNN是使用频谱图训练的,因此与TCNN相比,输入的维度减少了,这意味着模型参数的数量大大减少,因此过度拟合也减少了。由于参数数量较少,计算频谱图所需的额外时间大约由FCNN的较低执行时间补偿。在我们的分析中,FCNN在验证数据上表现出略好的性能,并且由于参数数量较少而大大降低了过拟合,这为GW探测器数据的分析提供了一种新的有前途的方法,未来可以通过使用更有效和更快的频谱图计算来进一步改进。

英文摘要:

Detection of gravitational waves (GW) from compact binary mergers provide a new window into multi-messenger astrophysics. The standard technique to determine the merger parameters is matched filtering, consisting in comparing the signal to a template bank. This approach can be time consuming and computationally expensive due to the large amount of experimental data which needs to be analyzed. In the attempt to find more efficient data analysis methods we develop a new frequency domain convolutional neural network (FCNN) to predict the merger masses from the spectrogram of the detector signal, and compare it to time domain neural networks (TCNN). Since FCNNs are trained using spectrograms, the dimension of the input is reduced as compared to TCNNs, implying a substantially lower number of model parameters, and consequently less over-fitting. The additional time required to compute the spectrogram is approximately compensated by the lower execution time of the FCNNs, due to the lower number of parameters. In our analysis FCNNs show a slightly better performance on validation data and a substantially lower over-fit, as expected due to the lower number of parameters, providing a new promising approach to the analysis of GW detectors data, which could be further improved in the future by using more efficient and faster computations of the spectrogram.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑