AI 中文总结
本研究利用引力波探测器网络结合深度学习技术，可估计核心坍缩超新星的峰值频率、旋转速率与信号振幅，当前代网络的参数估计可达30kpc，第三代天文台的探测距离提升近一个数量级，显著改善参数恢复与天区覆盖能力。
AI 中文摘要
核心坍缩超新星是最具前景但仍未被探测到的引力波源之一，未来的探测将为研究坍缩恒星内部发生的物理过程提供直接观测视角。本研究利用当前及未来的引力波探测器网络，结合核心坍缩超新星的核心反弹及反弹后早期特征信号，探究其对快速旋转核心坍缩超新星性质的约束能力。研究采用深度学习技术，从含噪探测器数据中估计峰值频率、旋转速率和信号振幅，并对比不同探测器网络配置的性能。结果显示，探测器网络可同时提升参数恢复能力与天区覆盖范围；对于当前代网络，峰值频率的估计可达约30千秒差距（kpc），而旋转速率和信号振幅在超过100千秒差距的距离上仍可恢复；第三代天文台则将这些探测距离提升了近一个数量级。
英文摘要
Core-collapse supernovae are among the most promising yet still undetected sources of gravitational waves. A future detection would provide a direct view of the physical processes occurring deep inside a collapsing star. In this work, we investigate how networks of current and future gravitational-wave detectors can constrain the properties of rapidly rotating core-collapse supernovae using their characteristic core-bounce and early post-bounce signals. Using deep-learning techniques, we estimate the peak frequency, rotation rate, and signal amplitude from noisy detector data and compare the performance of different detector-network configurations. We find that detector networks improve both parameter recovery and sky coverage. For current-generation networks, estimation of the peak frequency is possible out to about 30 kpc, while the rotation rate and signal amplitude remain recoverable out to distances exceeding 100 kpc. Third-generation observatories extend these distances by nearly an order of magnitude.
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