Rapid eccentric spin-aligned binary black hole waveform generation based on deep learning

TL;DR

This paper introduces SEOBNRE_AIq5e2, an AI-based surrogate model that significantly accelerates the generation of accurate eccentric, spin-aligned binary black hole waveforms, aiding gravitational wave data analysis.

Contribution

The paper presents a novel AI-driven surrogate model that speeds up eccentric BBH waveform generation while maintaining high accuracy, surpassing previous methods in efficiency.

Findings

Waveform generation speed of 4.3 ms per waveform.

Mean mismatch of 1.02 x 10^{-3} indicating high accuracy.

Supports mass ratios up to 5 and eccentricities below 0.2.

Abstract

Accurate waveform templates of binary black holes (BBHs) with eccentric orbits are essential for the detection and precise parameter estimation of gravitational waves (GWs). While SEOBNRE produces accurate time-domain waveforms for eccentric BBH systems, its generation speed remains a critical bottleneck in analyzing such systems. Accelerating template generation is crucial to data analysis improvement and valuable information extraction from observational data. We present SEOBNRE_AIq5e2, an innovative AI-based surrogate model that crafted to accelerate waveform generation for eccentric, spin-aligned BBH systems. SEOBNRE_AIq5e2 incorporates an advanced adaptive resampling technique during training, enabling the generation of eccentric BBH waveforms with mass ratios up to 5, eccentricities below 0.2, and spins $|\chi_z|$ up to 0.6. It achieves an impressive generation speed of 4.3 ms per waveform with a mean mismatch of $1.02 \times 10^{-3}$. With the exceptional accuracy and rapid performance, SEOBNRE_AIq5e2 emerges as a promising waveform template for future analysis of eccentric gravitational wave data.