Binary black-hole surrogate waveform catalog

This repository contains all publicly available numerical relativity surrogate data for waveforms produced by the Spectral Einstein Code. The base method for building surrogate models were originally described in Field et al., PRX 4, 031006 (2014). The more recent article specific to gwsurrogate is Field et al., Journal of Open Source Software, 10(107), 7073.

Several numerical relativity surrogate models are currently available in this catalog:

• Current models

  1. NRHybSur3dq8_CCE — This is a surrogate model for binary black hole systems built using CCE waveforms, capturing memory effects, with generic mass ratios but restricted to nonprecessing spins. Before constructing the surrogate, the NR waveforms are hybridized with post-Newtonian waveforms to include the early inspiral. Therefore this model covers full stellar mass range for for ground-based detectors. A paper describing it can be found at  Yoo et al., Phys. Rev. D 108, 064027 (2023). It is evaluated with the gwsurrogate Python package, which can be found on PyPI.

  2. NRHybSur2dq15 — This is a surrogate model for binary black hole systems with a high mass ratio (up to 15), but restricted to nonprecessing spins and no secondary spin. Before constructing the surrogate, the NR waveforms are hybridized with SEOBNRv4HM to include the early inspiral. Therefore this model covers 9.5 solar mass or higher total mass system for ground-based detectors. A paper describing it can be found at Yoo et al., Phys. Rev. D 106, 044001 (2022). It is evaluated with the gwsurrogate Python package, which can be found on PyPI.

  3. NRSur7dq4 — This is a surrogate model for binary black hole mergers with generic spins and mass ratios up to 4. A paper describing it can be found at Varma et al., Phys. Rev. Research 1, 033015 (2019). It is evaluated with the gwsurrogate Python package, which can be found on PyPI . Instructions for evaluating this surrogate can be found in this tutorial.

  4. NRSur7dq4v2 — This is an improved version of NRSur7dq4, built from the same underlying data. A paper describing it can be found at Ravishankar et al., arXiv:2609.07873. It is evaluated with the gwsurrogate Python pa
     

     📤 Share this page

     Found this useful? Share it with your network.

     𝕏XfFacebookinLinkedIn✆WhatsApp✈Telegram✉EmailMMendeley⧉Copy LinkR^GResearchGateAAcademia.edu

     ✓ Link copied! Paste it on ResearchGate / Academia.edu