Learning orbital dynamics of binary black hole systems from gravitational wave measurements
Learning orbital dynamics of binary black hole systems from gravitational wave measurements
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DOI：
10.1103/physrevresearch.3.043101
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发表时间：
2021-02
影响因子：
4.2
通讯作者：
B. Keith;Akshay Khadse;Scott E. Field
中科院分区：
文献类型：
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作者：
B. Keith;Akshay Khadse;Scott E. Field
我们介绍了引力波形反演策略，发现力学模型的二元黑洞（BBH）系统。我们表明，只有一个单一的时间序列（可能是嘈杂的）波形数据是必要的，以构建BBH系统的运动方程。从一类由前馈神经网络参数化的普适微分方程开始，我们的策略涉及构建一个合理的机械模型空间，并在该空间内进行物理约束优化，以最大限度地减少波形误差。我们将我们的方法应用到各种BBH系统，包括极端和可比的偏心和非偏心轨道的质量比系统。我们表明，所得到的微分方程适用于比训练间隔更长的持续时间，和相对论效应，如近日点进动，辐射反应，和轨道暴跌，自动占。这里概述的方法提供了一种新的，数据驱动的方法来研究二元黑洞系统的动力学。
We introduce a gravitational waveform inversion strategy that discovers mechanical models of binary black hole (BBH) systems. We show that only a single time series of (possibly noisy) waveform data is necessary to construct the equations of motion for a BBH system. Starting with a class of universal differential equations parameterized by feed-forward neural networks, our strategy involves the construction of a space of plausible mechanical models and a physics-informed constrained optimization within that space to minimize the waveform error. We apply our method to various BBH systems including extreme and comparable mass ratio systems in eccentric and non-eccentric orbits. We show the resulting differential equations apply to time durations longer than the training interval, and relativistic effects, such as perihelion precession, radiation reaction, and orbital plunge, are automatically accounted for. The methods outlined here provide a new, data-driven approach to studying the dynamics of binary black hole systems.