Early warning of coalescing neutron-star and neutron-star-black-hole binaries from the nonstationary noise background using neural networks
Early warning of coalescing neutron-star and neutron-star-black-hole binaries from the nonstationary noise background using neural networks
复制标题
DOI：
10.1103/physrevd.104.062004
复制
发表时间：
2021-04
影响因子：
5
通讯作者：
Hang Yu;R. Adhikari;R. Magee;S. Sachdev;Yanbei Chen
中科院分区：
文献类型：
--
作者：
Hang Yu;R. Adhikari;R. Magee;S. Sachdev;Yanbei Chen
多信使天文学的成功依赖于像LIGO和Virgo这样的引力波天文台，以提供涉及中子星（包括中子星双星和中子-星星-黑洞）的合并事件的及时警告，这进一步取决于LIGO的低频灵敏度，因为典型的中子星星会在这个波段停留几分钟。然而，目前LIGO的亚60 Hz灵敏度尚未达到其设计目标，并且多余的噪声可以在20 Hz以下超过一个数量级。它受到来自辅助控制回路的非线性耦合噪声的限制，这些噪声也是非平稳的，这对现实的预警管道提出了挑战。然而，基于机器学习的神经网络提供了同时提高低频灵敏度和减轻其非平稳性的方法，并以非常短的计算时间检测实时引力波信号。我们建议通过将主要的引力波读数和关键的辅助证人输入到复合神经网络来实现这一点。使用模拟数据与代表真实的LIGO探测器的特性，我们的机器学习为基础的神经网络可以减少非线性耦合噪声约5倍，并允许一个典型的二元中子星星（中子-星星-黑洞）被检测到合并前100秒（10秒）在40 Mpc（160 Mpc）的距离。如果能够进一步将噪声降低到基本极限，我们的神经网络可以分别实现对双星中子星和中子星黑洞的80 Mpc和240 Mpc距离的检测。因此，它表明，利用基于机器学习的神经网络是一个很有前途的方向，及时检测的电磁明亮的LIGO/Virgo源的合并。
The success of the multi-messenger astronomy relies on gravitational-wave observatories like LIGO and Virgo to provide prompt warning of merger events involving neutron stars (including both binary neutron stars and neutron-star-black-holes), which further depends critically on the low-frequency sensitivity of LIGO as a typical binary neutron star stays in this band for minutes. However, the current sub-60 Hz sensitivity of LIGO has not yet reached its design target and the excess noise can be more than an order of magnitude below 20 Hz. It is limited by nonlinearly coupled noises from auxiliary control loops which are also nonstationary, posing challenges to realistic early-warning pipelines. Nevertheless, machine-learning-based neural networks provide ways to simultaneously improve the low-frequency sensitivity and mitigate its nonstationarity, and detect the real-time gravitational-wave signal with a very short computational time. We propose to achieve this by inputting both the main gravitational-wave readout and key auxiliary witnesses to a compound neural network. Using simulated data with characteristic representing the real LIGO detectors, our machine-learning-based neural networks can reduce nonlinearly coupled noise by about a factor of 5 and allows a typical binary neutron star (neutron-star-black-hole) to be detected 100 s (10 s) before the merger at a distance of 40 Mpc (160 Mpc). If one can further reduce the noise to the fundamental limit, our neural networks can achieve detection out to a distance of 80 Mpc and 240 Mpc for binary neutron stars and neutron-star-black-holes, respectively. It thus demonstrates that utilizing machine-learning-based neural networks is a promising direction for the timely detection of the coalescence of electromagnetically bright LIGO/Virgo sources.