arXiv 2201.064612022-01-17gr-qcastro-ph.HEcs.LGstat.ML

使用机器学习参数化来自双中子星的合并后信号

Using machine learning to parametrize postmerger signals from binary neutron stars

University of Waterloo（滑铁卢大学）；Perimeter Institute for Theoretical Physics（圆周理论物理研究所）；Max Planck Institute for Gravitational Physics (Albert Einstein Institute)（马克斯·普朗克引力物理研究所（阿尔伯特·爱因斯坦研究所））；University of Guelph（圭尔夫大学）

Tim Whittaker, William E. East, Stephen R. Green, Luis Lehner, Huan Yang

发表信息：: Phys. Rev. D 105, 124021 (2022)
DOI：: 10.1103/PhysRevD.105.124021

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摘要：

人们对探测和表征由双中子星合并后振荡产生的引力波越来越感兴趣。这些信号包含了关于残余物的性质以及合并后过程的高密度和不平衡物理学的信息，这将补充任何电磁信号。然而，双中子星合并后波形的构造比双黑洞复杂得多：（i）在中子星状态方程和高密度物理学的其他方面存在理论上的不确定性，（ii）数值模拟费用昂贵，现有的数值模拟只覆盖参数空间的一小部分，数值精度有限，以及（iii）不清楚如何将理论不确定性参数化并在参数空间内插。在这项工作中，我们描述了使用一种机器学习方法，称为条件变分自动编码器（CVAE），构建超/大质量中子星残留信号的数值相对论模拟的基础上合并后的模型。CVAE提供了一个概率模型，该模型将训练数据中的不确定性编码在一组潜在参数中。我们估计训练这样的模型最终将需要$\sim 10^4$波形。然而，使用合成训练波形作为原理证明，我们表明CVAE可以用作准确的生成模型，并且它以有用的潜在表示对状态方程进行编码。

英文摘要：

There is growing interest in the detection and characterization of gravitational waves from postmerger oscillations of binary neutron stars. These signals contain information about the nature of the remnant and the high-density and out-of-equilibrium physics of the postmerger processes, which would complement any electromagnetic signal. However, the construction of binary neutron star postmerger waveforms is much more complicated than for binary black holes: (i) there are theoretical uncertainties in the neutron-star equation of state and other aspects of the high-density physics, (ii) numerical simulations are expensive and available ones only cover a small fraction of the parameter space with limited numerical accuracy, and (iii) it is unclear how to parametrize the theoretical uncertainties and interpolate across parameter space. In this work, we describe the use of a machine-learning method called a conditional variational autoencoder (CVAE) to construct postmerger models for hyper/massive neutron star remnant signals based on numerical-relativity simulations. The CVAE provides a probabilistic model, which encodes uncertainties in the training data within a set of latent parameters. We estimate that training such a model will ultimately require $\sim 10^4$ waveforms. However, using synthetic training waveforms as a proof-of-principle, we show that the CVAE can be used as an accurate generative model and that it encodes the equation of state in a useful latent representation.

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