Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate...

Area of Science:

• Astrophysics
• General Relativity
• Computational Physics

Background:

• Simulating binary black hole (BBH) mergers using Einstein's equations is computationally intensive, demanding extensive supercomputing resources.
• Numerical relativity (NR) simulations provide crucial data but are time-consuming, limiting their application in gravitational wave data analysis.

Purpose of the Study:

• To develop an accurate and efficient surrogate model for numerical relativity waveforms of nonspinning binary black hole coalescences.
• To enable rapid waveform generation for gravitational wave astronomy and data analysis.

Main Methods:

• Employed reduced-order modeling techniques to construct a surrogate model for BBH coalescence waveforms.
• Incorporated spherical-harmonic waveform modes up to ℓ=8.
• Assessed model uncertainty and predictive accuracy on unseen data.

Main Results:

• The surrogate model evaluates waveforms in milliseconds to seconds, a drastic reduction from the days to months required for NR simulations.
• The model demonstrates high accuracy, with prediction errors comparable to the NR code's numerical errors.
• Comparison with effective one body waveforms shows the surrogate model is significantly more faithful for advanced LIGO detectors.

Conclusions:

• The developed surrogate model offers a computationally efficient and accurate alternative to full NR simulations for nonspinning BBH mergers.
• This advancement can accelerate the analysis of gravitational wave signals from BBH coalescences, improving our understanding of these cosmic events.