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 $5.89\times10^{-4}$ to $5.34\times10^{-2}$ relative to the reference waveforms. Batch generation achieves a maximum speedup of about 1800 over reference waveform generation.