论文标题
重力波的卷积神经网络早期警报：频率下降
Convolutional neural network for gravitational-wave early alert: Going down in frequency
论文作者
论文摘要
我们在这里介绍了机器学习管道的最新开发，用于来自二进制中子星的重力波的预合并警报。这项工作始于我们以前的论文（PhysRevd.103.102003）中介绍的卷积神经网络，该网络在模拟的高斯噪声中搜索了三类的早期灵感，并用Ligo的设计敏感性功率 - 光谱密度着色。我们的新网络能够搜索任何类型的二进制中子星，它可以考虑所有可用的检测器，并且甚至比上一个事件更早地看到事件。我们在三种不同类型的噪声中研究方法的性能：高斯O3噪声，实际O3噪声和预测的O4噪声。我们表明，我们的网络在非高斯噪声方面的性能几乎与高斯噪声一样：我们的方法是强大的W.R.T.毛刺和人工制品以真实的噪音存在。尽管由于其信噪比太弱，因此无法在O3期间检测到的BNSS上触发，但我们希望我们的网络在O4期间发现约3个BNS，并且在3到88 s之间的合并之前的一段时间。
We present here the latest development of a machine-learning pipeline for pre-merger alerts from gravitational waves coming from binary neutron stars. This work starts from the convolutional neural networks introduced in our previous paper (PhysRevD.103.102003) that searched for three classes of early inspirals in simulated Gaussian noise colored with the design-sensitivity power-spectral density of LIGO. Our new network is able to search for any type of binary neutron stars, it can take into account all the detectors available, and it can see the events even earlier than the previous one. We study the performance of our method in three different types of noise: Gaussian O3 noise, real O3 noise, and predicted O4 noise. We show that our network performs almost as well in non-Gaussian noise as in Gaussian noise: our method is robust w.r.t. glitches and artifacts present in real noise. Although it would not have been able to trigger on the BNSs detected during O3 because their signal-to-noise ratio was too weak, we expect our network to find around 3 BNSs during O4 with a time before the merger between 3 and 88 s in advance.
