论文标题
在深神网络中识别宇宙学信息
Identifying Cosmological Information in a Deep Neural Network
论文作者
论文摘要
提出了一种基于图像的宇宙学参数来估计宇宙学参数的新方法。在本文中，我们证明了使用卷积神经网络（CNN）来限制暗物质粒子的质量。为此，我们使用密度对比度映射进行了一组具有不同暗物质粒子质量的N体模拟训练CNN并估算暗物质质量。提出的方法是基于摘要统计数据的互补方法，例如两点相关函数。我们将CNN的分类结果与从暗物质颗粒分布的两点相关性获得的结果进行比较，并发现CNN提供了更好的性能，我们使用了从随机的高斯模拟的图像来训练CNN，然后将其与通过N-Body Simulation和Twi-simulation and Twiper Correlation训练的CNN进行比较。随机受训练的CNN的性能与两点相关性相当。
A novel method images to estimate cosmological parameters based on images is presented. In this paper, we demonstrate the use of a convolutional neural network (CNN) for constraining the mass of dark matter particle. For this purpose, we perform a suite of N-body simulations with different dark matter particle masses to train CNN and estimate dark matter mass using a density-contrast map. The proposed method is complementary to the one based on summary statistics, such as two-point correlation function. We compare our CNN classification results with those obtained from the two-point correlation of the distribution of dark matter particles, and find that the CNN offers better performance In addition, we use images made from a random Gauss simulation to train a CNN, which is then compared with the CNN trained by N-body simulation and two-point correlation. The random Gauss-trained CNN has comparable performance to two-point correlation.
