Faculty and Student Publications
Document Type
Article
Publication Date
12-1-2021
Abstract
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 noneccentric orbits. We show that 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 data-driven approach to studying the dynamics of binary black hole systems.
Relational Format
journal article
Recommended Citation
Keith, B., Khadse, A., & Field, S. E. (2021). Learning orbital dynamics of binary black hole systems from gravitational wave measurements. Physical Review Research, 3(4), 043101. https://doi.org/10.1103/PhysRevResearch.3.043101
DOI
10.1103/PhysRevResearch.3.043101
Accessibility Status
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