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PreprintarXiv licencearXiv

A Transformer model for precessing binary black-hole merger waveforms calibrated to numerical relativity

Abstract

We present a Transformer model for merger-centered gravitational waveforms from precessing binary black holes. Given the mass ratio and two three-dimensional spin vectors, the network predicts quintic B-spline coefficients for all 21 signed spin-weighted spherical-harmonic strain modes through ℓ=4 on $[-1100,80]M$ (M being the total binary mass), enforcing binary-exchange and equatorial-reflection equivariance and yielding C4 waveforms in time with exact automatic differentiation. The model is pretrained on numerical-relativity (NR) waveforms from the SXS and RIT catalogues together with synthetic NRSur7dq4, SEOBNRv5PHM, and perturbative waveforms, and fine-tuned on SXS strain data. On 176 held-out SXS waveforms (165 physical systems), it achieves a mean balanced root-mean-square strain error of $0.023$ and a mean detector mismatch at 120 M⊙ of 0.059%.

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BibTeX
@misc{dong2026transformer,
  title = {{A Transformer model for precessing binary black-hole merger waveforms calibrated to numerical relativity}},
  author = {Jameson Dong},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.07223},
  eprint = {2611.07223},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.07223},
}
RIS
TY  - UNPB
TI  - A Transformer model for precessing binary black-hole merger waveforms calibrated to numerical relativity
AU  - Jameson Dong
PY  - 2026
DA  - 2026/11/04
PB  - arXiv
DO  - 10.48550/arxiv.2611.07223
UR  - https://arxiv.org/abs/2611.07223
AB  - We present a Transformer model for merger-centered gravitational waveforms from precessing binary black holes. Given the mass ratio and two three-dimensional spin vectors, the network predicts quintic B-spline coefficients for all 21 signed spin-weighted spherical-harmonic strain modes through ℓ=4 on $[-1100,80]M$ (M being the total binary mass), enforcing binary-exchange and equatorial-reflection equivariance and yielding C^4 waveforms in time with exact automatic differentiation. The model is pretrained on numerical-relativity (NR) waveforms from the SXS and RIT catalogues together with synthetic NRSur7dq4, SEOBNRv5PHM, and perturbative waveforms, and fine-tuned on SXS strain data. On 176 held-out SXS waveforms (165 physical systems), it achieves a mean balanced root-mean-square strain error of $0.023$ and a mean detector mismatch at 120 M_⊙ of 0.059%.
ER  -