NSF CDSE: Enabling Precise Constraints on Dark Energy

本提案的目标是开发先进的技术来计算准确和计算效率高的光度红移距离估计器，并减轻光度不确定性和在当前和即将进行的调查中发现的大量星系的群集测量的系统性。计算机代码将结合机器学习技术，结合监督和无监督学习算法。所开发的方法将适用于未来大型综合巡天望远镜进行的大规模巡天，该望远镜将对暗能量进行精确的限制。这项工作的更广泛影响包括培养一名博士后、一名研究生和一名本科生，以及开发一门数据科学在线课程。所有开发的计算工具都将提供给一般天文社区。

This goal of this proposal is to develop advanced techniques to calculate accurate and computationally efficient photometric redshift distance estimators, and to mitigate photometric uncertainties and systematics on clustering measures for galaxies found in large numbers in current and upcoming surveys. The computer codes will incorporate machine learning techniques with a combination of supervised and unsupervised learning algorithms. The methods developed will be applicable to the massive surveys to be undertaken by the future Large Synoptic Survey Telescope, which will, among other things, place precise constraints on Dark Energy. Broader impacts of the work include training of a postdoc, a graduate student, and undergraduate students, and development of a course in Data Science to be available on line. All computational tools developed will be made available to the general astronomical community.