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arXiv 2503.04534gr-qcastro-ph.IM
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通过机器学习将双黑洞的旋进-合并-铃降波形映射自黑洞微扰波形

Mapping inspiral-merger-ringdown waveforms of binary black holes from black hole perturbation waveforms by machine learning

Xing-Yu Zhong, Wen-Biao Han, Ling Sun

AI总结:

本研究提出基于闭式连续时间神经网络的波形映射方法及BHP2NRMLSur模型,将黑洞微扰理论波形高效映射为高精度的数值相对论波形,显著提升了引力波模板的计算效率。

摘要:

在噪声中识别微弱的引力波信号并估计源属性需要高精度的波形模板。数值相对论(NR)模拟能够提供最准确的波形。然而,由于高昂的计算成本,使用NR模拟在高维参数空间中计算波形模板极具挑战性。在本研究中,我们实现了一种新颖的波形映射方法,作为现有解析近似方法的替代方案,该方法基于闭式连续时间神经网络。这种基于机器学习的方法极大地提高了计算任意源参数(如双星质量比和组件黑洞自旋)波形模板的效率。基于该方法,我们提出了BHP2NRMLSur,这是一类模型(包括非自旋和自旋对齐模型),可将点粒子黑洞微扰理论波形映射为NR和代理波形。非自旋模型提供了高精度的波形,与NR波形的匹配度达到$\gtrsim 0.995$的水平。自旋对齐模型减少了所需的输入参数,从而提高了波形生成的效率——生成100,000个波形所需的时间比现有的NR代理模型少约50倍,且与来自Simulating eXtreme Spacetimes合作的NR波形相比,失配度<0.01。

英文摘要:

Identifying weak gravitational wave signals in noise and estimating the source properties require high-precision waveform templates. Numerical relativity (NR) simulations can provide the most accurate waveforms. However, it is challenging to compute waveform templates in high-dimensional parameter space using NR simulations due to high computational costs. In this work, we implement a novel waveform mapping method, which is an alternative approach to the existing analytical approximations, based on closed-form continuous-time neural networks. This machine-learning-based method greatly improves the efficiency of calculating waveform templates for arbitrary source parameters, such as the binary mass ratio and the spins of component black holes. Based on this method, we present \textit{BHP2NRMLSur}, a class of models (including nonspinning and spin-aligned ones) that maps point-particle black hole perturbation theory waveforms into NR and surrogate waveforms. The nonspinning model provides highly accurate waveforms that match the NR waveforms to the level of $\gtrsim 0.995$. The spin-aligned model reduces the required input parameters and hence improves the efficiency of the waveform generation -- it takes a factor of $\sim 50$ less time than existing NR surrogate models to generate $100,000$ waveforms, with a mismatch of $<0.01$ compared to the NR waveforms from the Simulating eXtreme Spacetimes collaboration.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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