Log In Sign Up

Orbital dynamics of binary black hole systems can be learned from gravitational wave measurements

by   Brendan Keith, et al.

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.


page 5

page 9


Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows

Physics-Informed Neural Networks (PINNs) offer a promising approach to s...

The Physics of Eccentric Binary Black Hole Mergers. A Numerical Relativity Perspective

Gravitational wave observations of eccentric binary black hole mergers w...

Copy the dynamics using a learning machine

Is it possible to generally construct a dynamical system to simulate a b...

Latent Neural Differential Equations for Video Generation

Generative Adversarial Networks have recently shown promise for video ge...

Revisit Geophysical Imaging in A New View of Physics-informed Generative Adversarial Learning

Seismic full waveform inversion (FWI) is a powerful geophysical imaging ...