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arXiv 2209.10089astro-ph.HE
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用卷积神经网络确定旋转核塌缩超新星的核结构和引力波的核物质状态方程

Determine the Core Structure and Nuclear Equation of State of Rotating Core-Collapse Supernovae with Gravitational Waves by Convolutional Neural Networks

Yang-Sheng Chao, Chen-Zhi Su, Ting-Yuan Chen, Daw-Wei Wang, Kuo-Chuan Pan

摘要:

探测到来自附近核心坍缩超新星的引力波将对超新星发动机和核物质状态方程施加有意义的约束。在这里,我们使用卷积神经网络模型来识别核心旋转速率,旋转长度尺度和核物质状态方程(EoS),使用Richers等人(2017)的1824个波形。利用从-10 ms到6 ms的地核反弹的引力波信号,可以实现旋转长度尺度($93\%$)和旋转速率($95\%$)分类的高预测精度。通过在瞬发对流阶段包括额外的48 ms信号,我们可以实现$96\%$对四个主要EoS组的分类准确性。结合上述三种模型,我们可以同时正确地预测核心自转速率、自转长度尺度和EoS,精度超过$85\%$。最后,将迁移学习方法应用于来自FLASH模拟的另外74个波形(Pan等人,2018),我们表明我们使用Richers波形的模型可以成功预测Pan波形的旋转速率,即使是平均绝对误差仅为0.32 rad s $^{-1}$的连续值。这些结果表明,更广泛的参数制度,我们的模型可以应用于识别核心坍缩超新星事件通过GW信号。

英文摘要:

Detecting gravitational waves from a nearby core-collapse supernova would place meaningful constraints on the supernova engine and nuclear equation of state. Here we use Convolutional Neural Network models to identify the core rotational rates, rotation length scales, and the nuclear equation of state (EoS), using the 1824 waveforms from Richers et al. (2017) for a 12 solar mass progenitor. High prediction accuracy for the classifications of the rotation length scales ($93\%$) and the rotational rates ($95\%$) can be achieved using the gravitational wave signals from -10 ms to 6 ms core bounce. By including additional 48 ms signals during the prompt convection phase, we could achieve $96\%$ accuracy on the classification of four major EoS groups. Combining three models above, we could correctly predict the core rotational rates, rotation length scales, and the EoS at the same time with more than $85\%$ accuracy. Finally, applying a transfer learning method for additional 74 waveforms from FLASH simulations (Pan et al. 2018), we show that our model using Richers' waveforms could successfully predict the rotational rates from Pan's waveforms even for a continuous value with a mean absolute errors of 0.32 rad s$^{-1}$ only. These results demonstrate a much broader parameter regimes our model can be applied for the identification of core-collapse supernova events through GW signals.

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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