Optimizing the Placement of Numerical Relativity Simulations using a Mismatch Predicting Neural Network

Abstract

Gravitational wave observations from merging compact objects are becoming commonplace, and as detectors improve and gravitational wave sources become more varied, it is increasingly important to have dense and expansive template banks of predicted gravitational waveforms. Since numerical relativity is the only way to fully solve the non-linear merger regime of general relativity for comparably massed systems, numerical relativity simulations are critical for gravitational wave detection and analysis. These simulations are computationally expensive, with each simulation placing one point within the high dimensional parameter space of binary black hole coalescences. This makes it important to have a method of placing new simulations in ways that use our computational resources optimally while ensuring sufficient coverage of the parameter space. Accomplishing this requires predicting the impact of a new set of parameters before performing the simulation. To this effect, this paper introduces a neural network to predict the mismatch between the gravitational waves of two binary systems. Using this network, we then show how we can propose new numerical relativity simulations that will provide the most benefit. We also use the network to identify gaps in existing public catalogs and identify degeneracies in the binary black hole parameter space.

14 pages, 11 figures; additional small details added

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Cited by in corpus (6)

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• Systematic biases due to waveform mismodeling in parametrized post-Einsteinian tests of general relativity: The impact of neglecting spin precession and higher modes
• Flexible mapping of ringdown amplitudes for nonprecessing binary black holes
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