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.

翻译：人们对从后合并器对二进制中子星的状态和高密度物理学其他方面的中子-星级方程式的重力波的探测和定性越来越感兴趣。这些信号包含关于后合并过程的残余和高密度和超平衡物理学性质的信息,这将补充任何电磁信号。然而,建造二进制中子恒星后合并波形比二进制黑洞复杂得多:(一) 在中子-星级方程式中,状态和高密度物理学其他方面的中子-星级方程式存在理论不确定性,(二) 数字的精度模拟费用昂贵,可用模拟仅涵盖少量参数空间,但数字精确度有限,以及(三) 如何使理论不确定性和跨参数空间的内压化。在这项工作中,我们描述使用一个称为有条件变异性自动电解元模型(CVAE)的机器学习方法,用于为超/中子级的中值恒星再置信号模型,在数字-数字-数字4的方程参数参数中,我们用一个数字-变压模型来进行模拟的变压性模型的模拟。