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arXiv 2307.03435gr-qcastro-ph.HE
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将黑洞残骸代理模型推广至极端质量比

Extending black-hole remnant surrogate models to extreme mass ratios

Matteo Boschini, Davide Gerosa, Vijay Varma, Cristobal Armaza, Michael Boyle, Marceline S. Bonilla, Andrea Ceja, Yitian Chen, Nils Deppe, Matthew Giesler, Lawre… 展开作者

Matteo Boschini, Davide Gerosa, Vijay Varma, Cristobal Armaza, Michael Boyle, Marceline S. Bonilla, Andrea Ceja, Yitian Chen, Nils Deppe, Matthew Giesler, Lawrence E. Kidder, Prayush Kumar, Guillermo Lara, Oliver Long, Sizheng Ma, Keefe Mitman, Peter James Nee, Harald P. Pfeiffer, Antoni Ramos-Buades, Mark A. Scheel, Nils L. Vu, Jooheon Yoo

AI总结:

提出一种机器学习方法,通过结合相当质量数值模拟与极端质量比解析预测,扩展 NRSur7dq4Remnant 模型,构建出适用于任意质量比且精度更高的黑洞残骸代理模型 NRSur7dq4EmriRemnant。

摘要:

用于黑洞并合波形和残骸的数值相对论代理模型已成为引力波天文学中的重要工具。尽管能产生非常精确的预测,但其适用性仅限于数值相对论模拟可用且计算上可行的参数空间区域。值得注意的是,这排除了极端质量比的情况。我们提出了一种机器学习方法,将现有和未来的数值相对论代理模型的有效性扩展至测试粒子极限,特别针对并合后黑洞残骸的质量和自旋。我们的模型在相当质量的数值相对论模拟和极端质量比的解析预测上进行了训练。我们扩展了高斯过程回归模型 NRSur7dq4Remnant,通过交叉验证评估其性能,并针对额外的数值相对论运行测试了其准确性。我们的拟合模型被命名为 NRSur7dq4EmriRemnant,达到了与现有残骸模型相当或更高的准确性,同时为任意质量比提供了稳健的预测。

英文摘要:

Numerical-relativity surrogate models for both black-hole merger waveforms and remnants have emerged as important tools in gravitational-wave astronomy. While producing very accurate predictions, their applicability is limited to the region of the parameter space where numerical-relativity simulations are available and computationally feasible. Notably, this excludes extreme mass ratios. We present a machine-learning approach to extend the validity of existing and future numerical-relativity surrogate models toward the test-particle limit, targeting in particular the mass and spin of post-merger black-hole remnants. Our model is trained on both numerical-relativity simulations at comparable masses and analytical predictions at extreme mass ratios. We extend the gaussian-process-regression model NRSur7dq4Remnant, validate its performance via cross validation, and test its accuracy against additional numerical-relativity runs. Our fit, which we dub NRSur7dq4EmriRemnant, reaches an accuracy that is comparable to or higher than that of existing remnant models while providing robust predictions for arbitrary mass ratios.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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