AI 中文总结
该研究利用卷积神经网络，通过核心坍缩超新星引力波信号在LVK干涉测量噪声中对核状态方程分类，以五个CCSN模拟为示例分析数据，在1千秒差距处分类准确率高，有望扩展到下一代天文台，整体及各EOS类分类性能强。
AI 中文摘要
本文提出了一种用于对核状态方程（EOS）进行分类的卷积神经网络（CNN）方法。以五个仅在EOS方面不同的二维核心坍缩超新星（CCSN）模拟为示例，分析了在O3b LIGO - Virgo - KAGRA（LVK）观测运行中，从1、5和10千秒差距的银河源距离的实际干涉测量数据中重建的高频特征（HFF）初始斜率估计值。CNN分类器在1千秒差距处的总体准确率为98.58%，在5千秒差距处为52.43%，在10千秒差距处区分EOS类别的能力有效丧失。1千秒差距处的成功分类表明该方法可扩展到下一代天文台。宇宙探测器和爱因斯坦望远镜预期的灵敏度提高可能使在当前距离约十倍处实现可比的分类性能。更详细的性能指标，包括宏观平均的一对其余（OvR）曲线下面积（AUC），在1千秒差距处的值为0.97和0.98。这些结果表明在整个EOS类集以及单个类中都具有很强的分类性能。
英文摘要
This paper presents a convolutional neural network (CNN) approach to classifying the nuclear equation of state (EOS). As illustrative examples, we use five two-dimensional core-collapse supernova (CCSN) simulations that differ only in their EOS. We analyze estimates of the initial slope of the high-frequency feature (HFF) reconstructed in real interferometric data from the O3b LIGO-Virgo-KAGRA (LVK) observing run at Galactic source distances of 1, 5, and 10 kpc. The CNN classifier achieves an overall accuracy of 98.58% at 1 kpc and 52.43% at 5 kpc. At 10 kpc, its ability to distinguish among the EOS classes is effectively lost. The successful EOS classification at 1 kpc suggests that this approach may be scalable to next-generation observatories. The expected order-of-magnitude sensitivity improvements of Cosmic Explorer and the Einstein Telescope could enable comparable classification performance at approximately ten times the current distance. More detailed performance metrics, including the macro-averaged one-vs-rest (OvR) area under the curve (AUC), yield values of 0.97 and 0.98 at 1 kpc. These results indicate strong classification performance both across the complete set of EOS classes and for the individual classes.
Comments29 pages, 5 figures
