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Published September 15, 2023 | Published
Journal Article Open

Numerical relativity surrogate model with memory effects and post-Newtonian hybridization

Abstract

Numerical relativity simulations provide the most precise templates for the gravitational waves produced by binary black hole mergers. However, many of these simulations use an incomplete waveform extraction technique—extrapolation—that fails to capture important physics, such as gravitational memory effects. Cauchy-characteristic evolution (CCE), by contrast, is a much more physically accurate extraction procedure that fully evolves Einstein's equations to future null infinity and accurately captures the expected physics. In this work, we present a new surrogate model, NRHybSur3dq8_CCE, built from CCE waveforms that have been mapped to the post-Newtonian (PN) BMS frame and then hybridized with PN and effective one-body (EOB) waveforms. This model is trained on 102 waveforms with mass ratios q ≤ 8 and aligned spins χ_(1z),χ_(2z) ∈ [−0.8,0.8]. The model spans the entire LIGO-Virgo-KAGRA (LVK) frequency band (with flow = 20  Hz) for total masses M ≳ 2.25 M_⊙ and includes the ℓ ≤ 4 and (ℓ,m)=(5,5) spin-weight −2 spherical harmonic modes, but not the (3, 1), (4, 2) or (4, 1) modes. We find that NRHybSur3dq8_CCE can accurately reproduce the training waveforms with mismatches ≲2 × 10⁻⁴ for total masses 2.2 5M_⊙ ≤ M ≤ 300 M_⊙ and can, for a modest degree of extrapolation, capably model outside of its training region. Most importantly, unlike previous waveform models, the new surrogate model successfully captures memory effects.

Copyright and License

© 2023 American Physical Society.

Acknowledgement

This work was supported in part by the Sherman Fairchild Foundation and by NSF Grants No. PHY-2011961, No. PHY-2011968, and No. OAC-2209655 at Caltech, and NSF Grants No. PHY-2207342 and No. OAC-2209655 at Cornell. spectre uses charm++/converse [124], which was developed by the Parallel Programming Laboratory in the Department of Computer Science at the University of Illinois at Urbana-Champaign. V. V. acknowledges support from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant agreement No. 896869. L. C. S. was partially supported by NSF CAREER Award PHY-2047382. S. E. F acknowledges partial support from NSF Grant PHY-2110496 and by UMass Dartmouth's Marine and Undersea Technology (MUST) Research Program funded by the Office of Naval Research (ONR) under Grant No. N00014-23-1–2141. This material is based upon work supported by NSF's LIGO Laboratory which is a major facility fully funded by the NSF. This project made use of python libraries including scipy [125] and numpy [126], and the figures were produced using matplotlib [127] and seaborn [128].

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Additional details

Created:
October 16, 2023
Modified:
October 16, 2023