An autoencoder-based surrogate waveform model for quasi-circular binary-black-hole mergers

TL;DR

This paper introduces AESur3dq8, an autoencoder-based surrogate model for rapid, accurate generation of gravitational waveforms from quasi-circular binary black hole mergers, significantly speeding up template creation for gravitational-wave analysis.

Contribution

The paper presents a novel autoencoder-based surrogate waveform model that enables fast and accurate generation of large waveform template banks for gravitational-wave astronomy.

Findings

Waveforms achieve mismatches of order 10^{-4} with Numerical Relativity.

Parameter estimation results are consistent with LIGO-Virgo-KAGRA analyses.

Model can generate millions of waveforms in under a second.

Abstract

The generation of accurate waveforms from binary black hole (BBH) mergers is a major effort in Gravitational-Wave Astronomy. In recent years, machine-learning-based surrogate models for BBH waveforms have been proposed. Those offer the potential to dramatically accelerate waveform generation while maintaining accuracy competitive with that of traditional waveform approximants. In this work, we investigate the viability of autoencoders as generative models for gravitational-wave signals from quasi-circular BBH mergers. We introduce AESur3dq8, a novel surrogate waveform model based on autoencoders that enables the rapid and accurate construction of large template banks, producing millions of waveforms in under a second using modest computational resources. The model is trained on the numerical-relativity-informed surrogate NRHybSur3dq8 and subsequently fine-tuned using the SXS catalog of BBH simulations. We demonstrate that waveforms generated by AESur3dq8 achieve mismatches of order $10^{-4}$ with respect to Numerical Relativity waveforms, and that parameter estimation performed with these templates yields results fully consistent with those reported by the LIGO-Virgo-KAGRA Collaboration for observed gravitational-wave events.