进化深度学习识别回避区的Galerkin
Evolutionary Deep Learning to Identify Galaxies in the Zone of Avoidance
摘要：
由于高恒星密度和灭绝，回避带使得天文学家很难将低纬度的星系编入我们的银河系平面。然而，拥有一张完整的星系天空图在天文学的许多研究领域都很重要。有许多未分类的光源在回避区，因此，重要的是，有一个准确的自动化系统，以确定和分类星系在这一地区。本研究旨在评估使用进化算法来进化卷积神经网络（CNN）的拓扑结构和配置以自动识别回避区中的星系的效率和准确性。监督学习方法用于包含近红外图像的数据。输入图像分辨率和进化算法所需的近红外通带的数量也进行了分析，而最好的进化CNN的准确性相比，其他CNN的变种。
英文摘要：
The Zone of Avoidance makes it difficult for astronomers to catalogue galaxies at low latitudes to our galactic plane due to high star densities and extinction. However, having a complete sky map of galaxies is important in a number of fields of research in astronomy. There are many unclassified sources of light in the Zone of Avoidance and it is therefore important that there exists an accurate automated system to identify and classify galaxies in this region. This study aims to evaluate the efficiency and accuracy of using an evolutionary algorithm to evolve the topology and configuration of Convolutional Neural Network (CNNs) to automatically identify galaxies in the Zone of Avoidance. A supervised learning method is used with data containing near-infrared images. Input image resolution and number of near-infrared passbands needed by the evolutionary algorithm is also analyzed while the accuracy of the best evolved CNN is compared to other CNN variants.
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。
