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arXiv 2004.09524gr-qcastro-ph.IMcs.AI
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物理学启发的深度学习来表征准圆形,旋转,非旋进二元黑洞合并的信号流形

Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers

National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校国家超级计算应用中心);Department of Physics, University of Illinois at Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校物理系);Department of Astronomy, University of Illinois at Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校天文学系);Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign(伊利诺伊大学厄巴纳-香槟分校电气与计算机工程系)

Asad Khan, E. A. Huerta, Arnav Das

摘要:

双黑洞合并的自旋分布包含了关于这些物体形成通道的关键信息,以及它们形成、演化和合并的天体物理环境。为了量化深度学习对描述准圆形、自旋、非旋进二元黑洞合并信号流形的适用性,我们引入了一个修改后的WaveNet版本,该版本采用了一种新的优化方案,该方案结合了天体物理学黑洞自旋特性的广义相对论约束。使用在NRHybSur3dq 8有效性范围内生成的150万个$\ell=|m|=2$波形对神经网络模型进行训练、验证和测试,即,质量比$q\leq8$和单个黑洞自旋$ | s^z_{\{1,\,2\}} | \leq 0.8$。使用这个神经网络模型,我们量化了在没有噪声的情况下,我们可以准确地推断出黑洞合并的天体物理参数。我们通过计算测试数据集中的波形与相应信号之间的重叠来做到这一点,这些信号的质量比和单个自旋由我们的神经网络预测。我们发现,收敛的高性能计算和物理启发的优化算法,使准确重建的质量比和个别自旋的二进制黑洞合并在考虑的参数空间。这是在实际探测场景中利用物理学启发的深度学习模型重建二元黑洞合并自旋分布的重要一步。

英文摘要:

The spin distribution of binary black hole mergers contains key information concerning the formation channels of these objects, and the astrophysical environments where they form, evolve and coalesce. To quantify the suitability of deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers, we introduce a modified version of WaveNet trained with a novel optimization scheme that incorporates general relativistic constraints of the spin properties of astrophysical black holes. The neural network model is trained, validated and tested with 1.5 million $\ell=|m|=2$ waveforms generated within the regime of validity of NRHybSur3dq8, i.e., mass-ratios $q\leq8$ and individual black hole spins $ | s^z_{\{1,\,2\}} | \leq 0.8$. Using this neural network model, we quantify how accurately we can infer the astrophysical parameters of black hole mergers in the absence of noise. We do this by computing the overlap between waveforms in the testing data set and the corresponding signals whose mass-ratio and individual spins are predicted by our neural network. We find that the convergence of high performance computing and physics-inspired optimization algorithms enable an accurate reconstruction of the mass-ratio and individual spins of binary black hole mergers across the parameter space under consideration. This is a significant step towards an informed utilization of physics-inspired deep learning models to reconstruct the spin distribution of binary black hole mergers in realistic detection scenarios.

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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