NRSur7dq4v2：一种精度更高的多域进动代理模型
NRSur7dq4v2: A multi-domain precessing surrogate model with improved accuracy
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中文总结 AI 辅助
针对NRSur7dq4在并合-铃宕段精度不足的问题，提出多域扩展NRSur7dq4v2，通过重叠子域独立误差控制，将残余质量与自旋估计精度提升3-10倍，并实现四倍加速。
中文摘要 AI 辅助
数值相对论模拟为双黑洞并合提供了最精确的波形，但直接用于引力波数据分析时计算成本过高，令人望而却步。代理模型克服了这一成本问题，而NRSur7dq4也因此常用于参数估计。然而，文献中有证据表明，NRSur7dq4在波形的并合-铃宕部分的精度仍有提升空间，而该部分对高质量双黑洞事件的分析尤为重要。基于这些观察，我们构建了NRSur7dq4v2，这是NRSur7dq4的一种多域扩展，其中重叠的时间子域允许对波形的旋进和并合-铃宕部分在平滑组合之前进行更严格、独立的误差控制。为评估新模型在并合-铃宕区域的性能，我们通过对代理波形铃宕部分的准正则拟合来推断残余黑洞的质量和自旋。我们发现，NRSur7dq4v2产生的残余质量和自旋估计显著改善，增益约为NRSur7dq4的3到10倍。与NRSur7dq4相比，NRSur7dq4v2还包含了高达ℓ=5的模态，对某些次主导模态进行了更精确的建模，并引入了一个运行时模型复杂度特性，使用户能够直接控制评估成本与精度之间的权衡。最后，在模型开发的同时，我们优化了gwsurrogate软件包，使进动代理模型实现了四倍加速。由此通过gwsurrogate调用的进动代理模型现在比对应的LALSimulation实现略快。
英文摘要
Numerical relativity simulations provide the most accurate waveforms for binary black hole coalescences, but are prohibitively expensive for direct use in gravitational-wave data analysis. Surrogate models overcome this cost, and NRSur7dq4 is commonly used in parameter estimation for this reason. However, there is evidence in the literature that NRSur7dq4's accuracy could be improved in the merger-ringdown portion of the waveform, which is particularly important for the analysis of high-mass binary black hole events. Motivated by these observations, we construct NRSur7dq4v2, a multi-domain extension of NRSur7dq4 in which overlapping temporal subdomains allow tighter, independent error control over the inspiral and merger-ringdown portions of the waveform before they are smoothly combined. To assess the new model's performance in the merger-ringdown regime, we infer the mass and spin of the remnant black hole from quasi-normal fits to the ringdown portion of the surrogate waveform. We find that NRSur7dq4v2 produces significantly improved remnant mass and spin estimates, with gains of roughly factors of $3$ to $10$ over NRSur7dq4. Compared to NRSur7dq4, NRSur7dq4v2 also includes modes up to $\ell=5$, includes more accurate modeling of certain subdominant modes, and introduces a runtime model-complexity feature that gives users direct control over the tradeoff between evaluation cost and accuracy. Finally, alongside the model development, we have optimized the gwsurrogate package to achieve a fourfold speedup for precessing surrogates. The resulting precessing surrogates called through gwsurrogate are now slightly faster than their LALSimulation counterparts.
发表机构
- University of Massachusetts, Dartmouth（麻省大学达特茅斯分校）
- Cornell University（康奈尔大学）
- The University of Mississippi（密西西比大学）
- California Institute of Technology（加州理工学院）
机构由 AI 辅助整理，请以论文原文为准。
