基于遗传算法的SKA碟形优化
Optimal SKA Dish Configuration using Genetic Algorithms
摘要：
平方公里阵列（SKA）是一种设计在70 MHz和10 GHz之间运行的射电望远镜。由于这种大带宽，SKA将由不同的收集器组成，即天线和碟形天线，以充分覆盖频率范围。为了处理这种带宽，必须在开发的初始阶段设计和引入创新的馈源和探测器。此外，所需的分辨率水平只能通过碟形天线和天线的突破性配置来实现。由于SKA需要大面积的收集区域和提供承诺的科学所需的规格，站内的碟形天线和天线的配置是一个重要的问题。这项研究建立在Cohanim等人（2004），Hassan等人（2005）和Grigorescu等人（2009）之前所做的工作的基础上，以进一步研究机器学习技术的适用性，以确定SKA内收集元件的最佳配置。这项工作主要使用遗传算法来搜索一个大的空间的最优布局。每个遗传步骤都为种群提供了候选个体，每个候选个体都编码了一个可能的解决方案。这些是随机生成的，或者通过先前编码的组合创建的。在这项研究中，一些健身功能，排名个人在人口中的菜配置进行了调查。紫外线密度，连接线长度和功率谱被认为是确定一个良好的盘布局。
英文摘要：
The Square Kilometre Array (SKA) is a radio telescope designed to operate between 70MHz and 10GHz. Due to this large bandwidth, the SKA will be built out of different collectors, namely antennas and dishes to cover the frequency range adequately. In order to deal with this bandwidth, innovative feeds and detectors must be designed and introduced in the initial phases of development. Moreover, the required level of resolution may only be achieved through a groundbreaking configuration of dishes and antennas. Due to the large collecting area and the specifications required for the SKA to deliver the promised science, the configuration of the dishes and the antennas within stations is an important question. This research builds on the work done before by Cohanim et al. (2004), Hassan et al. (2005) and Grigorescu et al. (2009) to further investigate the applicability of machine learning techniques to determine the optimum configurations for the collecting elements within the SKA. This work primarily uses genetic algorithms to search a large space of optimum layouts. Every genetic step provides a population with candidate individuals each of which encodes a possible solution. These are randomly generated or created through the combination of previous encodings. In this study, a number of fitness functions that rank individuals within a population of dish configurations are investigated. The UV density, connecting wire length and power spectra are considered to determine a good dish layout.
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。
