Gravitational waves from binary neutron star mergers provide insights into dense matter physics and strong-field gravity, but waveform modeling remains computationally challenging. We develop a deep generative model for gravitational waveforms from binary neutron star mergers, covering the late inspiral, merger, and ringdown, incorporating precession and tidal effects. Using the conditional autoencoder, our model efficiently generates waveforms with high precision across a broad parameter space, including component masses $(m_1,m_2)$, spin components $(S_{1x},S_{1y},S_{1z},S_{2x},S_{2y},S_{2z})$, and tidal deformability $({\mathrm{\Lambda }}_1,{\mathrm{\Lambda }}_2)$. Trained on $1\times {10}^6$ waveforms from the IMRPhenomXP_NRTidalv2 waveform model, our model achieves a mean mismatch of $2.13\times {10}^{-3}$. The model accelerates waveform generation. For a single sample, it requires 0.12 s (s), compared to 0.66 s for IMRPhenomXP_NRTidalv2 making it approximately 5 times faster. When generating 1000 waveforms, the network completes the task in 0.75 s, while IMRPhenomXP_NRTidalv2 requires 7.12 s, making it approximately 10 times faster. This speed advantage enables rapid parameter estimation and real-time gravitational wave searches. With higher precision, it will support low-latency detection and broader applications in multimessenger astrophysics.

Close

PrevNext

View figure in article

• Gravitational waves
• Binary stars
• Neutron stars & pulsars
• Deep learning