A Unified AI Framework for Rapid and Accurate Waveform Generation of Compact Binary Coalescences
Abstract
The astrophysical discovery potential of future gravitational wave detectors will hinge on accurate and rapid waveform generation. Third generation detectors are expected to observe many long duration compact binary coalescence signals that can probe binary formation and evolution as well as the nature of matter under extreme conditions. We develop a unified framework that combines physical modeling with deep learning. The framework uses an analytic residual spectral representation, shared learning, and decoders for each binary type. It generates waveforms for binary black holes, binary neutron stars, and neutron star black hole binaries, providing a single unified generator for all compact binary coalescences. The generated waveforms include the physical effects described by each reference model, such as spin precession, higher order modes, matter effects, and postmerger radiation for binary neutron stars. The waveforms span hundreds to thousands of cycles, with mean mismatches ranging from 5.89×10−4 to 5.34×10−2 relative to the reference waveforms. Batch generation achieves a maximum speedup of about 1800 over reference waveform generation.
Related
- Line-of-Sight Acceleration-driven waveform corrections for unequal-mass compact binary coalescences
- CosmoForge. I. A unified framework for quadratic maximum likelihood power spectrum estimation and pixel-based likelihood analysis
- νGundam: A unified framework for point-redshift and probability-density-function-aware spatial correlation functions
- Type Ia supernova population machine – I. A unified cosmological simulation–binary synthesis framework establishing non-universal delay-time distributions and cosmic progenitor-channel dominance crossover
- Line-of-sight Acceleration Driven Effects in Eccentric Compact Binary Coalescences
BibTeX
@misc{sun2026unified,
title = {{A Unified AI Framework for Rapid and Accurate Waveform Generation of Compact Binary Coalescences}},
author = {Mengfei Sun and Jin Li and Yuanhong Zhong and Jie Wu and Borui Wang and Xianghe Ma and Minghui Zhang},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.04713},
eprint = {2611.04713},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.04713},
}
RIS
TY - UNPB
TI - A Unified AI Framework for Rapid and Accurate Waveform Generation of Compact Binary Coalescences
AU - Mengfei Sun
AU - Jin Li
AU - Yuanhong Zhong
AU - Jie Wu
AU - Borui Wang
AU - Xianghe Ma
AU - Minghui Zhang
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.04713
UR - https://arxiv.org/abs/2611.04713
AB - The astrophysical discovery potential of future gravitational wave detectors will hinge on accurate and rapid waveform generation. Third generation detectors are expected to observe many long duration compact binary coalescence signals that can probe binary formation and evolution as well as the nature of matter under extreme conditions. We develop a unified framework that combines physical modeling with deep learning. The framework uses an analytic residual spectral representation, shared learning, and decoders for each binary type. It generates waveforms for binary black holes, binary neutron stars, and neutron star black hole binaries, providing a single unified generator for all compact binary coalescences. The generated waveforms include the physical effects described by each reference model, such as spin precession, higher order modes, matter effects, and postmerger radiation for binary neutron stars. The waveforms span hundreds to thousands of cycles, with mean mismatches ranging from 5.89×10^−4 to 5.34×10^−2 relative to the reference waveforms. Batch generation achieves a maximum speedup of about 1800 over reference waveform generation.
ER -