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

TL;DR

This paper introduces a neural network to predict mismatches in gravitational waveforms, optimizing the placement of numerical relativity simulations for better coverage of the parameter space in gravitational wave research.

Contribution

The paper presents a novel neural network model that predicts waveform mismatches, enabling efficient placement of simulations and identification of gaps in existing catalogs.

Findings

Neural network accurately predicts waveform mismatches.

Optimized placement of simulations improves parameter space coverage.

Identifies gaps and degeneracies in existing waveform catalogs.

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.