论文标题
通过深度学习从旋转核心倒塌引力波分类状态方程
Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning
论文作者
论文摘要
在本文中，我们试图回答“鉴于旋转的核心崩溃引力波信号，我们可以确定其状态核方程吗？”。为了回答这个问题，我们采用深层卷积神经网络来学习嵌入旋转核心塌陷引力波（GW）信号中的视觉和时间模式，以预测状态的核方程（EOS）。使用Richers等人的1824年旋转核心崩溃GW模拟。 （2017年），有18种不同的核EOS，我们认为这是经典的多级图像分类和序列分类问题。在测试集中，我们最多达到72 \％正确的分类，如果我们考虑“前5个”最可能的标签，则最高可达97 \％，表明旋转核心崩溃的GW信号对核EOS存在中等且可测量的依赖性。
In this paper, we seek to answer the question "given a rotating core collapse gravitational wave signal, can we determine its nuclear equation of state?". To answer this question, we employ deep convolutional neural networks to learn visual and temporal patterns embedded within rotating core collapse gravitational wave (GW) signals in order to predict the nuclear equation of state (EOS). Using the 1824 rotating core collapse GW simulations by Richers et al. (2017), which has 18 different nuclear EOS, we consider this to be a classic multi-class image classification and sequence classification problem. We attain up to 72\% correct classifications in the test set, and if we consider the "top 5" most probable labels, this increases to up to 97\%, demonstrating that there is a moderate and measurable dependence of the rotating core collapse GW signal on the nuclear EOS.
