论文标题
使用深度学习方法探索重力波检测和参数推断
Exploring gravitational-wave detection and parameter inference using Deep Learning methods
论文作者
论文摘要
我们探索使用深度学习（DL）算法从二进制黑洞（BBH）合并中检测引力波（GW）的机器学习方法。 DL网络是通过从BBH合并获得的重力波形训练的，该引力波形从5到100太阳能质量和从100 MPC到至少为2000 MPC的范围内随机采样的分量质量。 GW信号波形从高级LIGO和高级处女座探测器的O2运行中注入公共数据，在时间窗口中与已知检测到的信号不一致。我们证明，在我们分析中考虑的范围内，在检测到更近的信号时，在检测到更接近信号的距离时，通过GW信号波形训练的DL算法仍然显示出很高的精度。此外，通过将三探测器网络的结果组合在唯一的RGB图像中，单个检测器性能的提高了多达70％。此外，我们训练一个回归网络，以对BBH频谱数据进行参数推断，并将该网络应用于GWTC-1和GWTC-2目录中的事件。在没有明显优化我们的算法的情况下，我们获得的结果与Ligo-Virgo协作的已发布结果一致。特别是，我们对chirp质量的预测是兼容的（最多3 $σ$），其正式值90％
We explore machine learning methods to detect gravitational waves (GW) from binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL networks are trained with gravitational waveforms obtained from BBH mergers with component masses randomly sampled in the range from 5 to 100 solar masses and luminosity distances from 100 Mpc to, at least, 2000 Mpc. The GW signal waveforms are injected in public data from the O2 run of the Advanced LIGO and Advanced Virgo detectors, in time windows that do not coincide with those of known detected signals. We demonstrate that DL algorithms, trained with GW signal waveforms at distances of 2000 Mpc, still show high accuracy when detecting closer signals, within the ranges considered in our analysis. Moreover, by combining the results of the three-detector network in a unique RGB image, the single detector performance is improved by as much as 70%. Furthermore, we train a regression network to perform parameter inference on BBH spectrogram data and apply this network to the events from the the GWTC-1 and GWTC-2 catalogs. Without significant optimization of our algorithms we obtain results that are mostly consistent with published results by the LIGO-Virgo Collaboration. In particular, our predictions for the chirp mass are compatible (up to 3$σ$) with the official values for 90% of events
