Large Scale Structure Studies on a Value Added Galaxy Catalog

新的统计和机器学习技术将创建一个更好的星系目录。 该目录将联合收割机从几个现有的调查信息。 一种被称为“光度红移”的技术将通过研究星系的颜色来提供速度和距离信息。 其结果将是最广泛的地区和最深的目录。 它将是与其他大面积调查，特别是宇宙微波背景（CMB）进行比较的理想选择。 这些研究将为宇宙学和暗能量提供线索。 他们甚至可能表明是否需要一个更奇特的引力理论。 通过专题课程提供教育。 与国家实验室有联系。 该项目将创建一个称为PS1* 的综合增值星系目录，用于大尺度结构（LSS）的统计研究。 在利用支持向量机和斯隆数字巡天数据的训练集为第一个PanSTARRS星表（PS1）开发恒星-星系分离算法的经验基础上，将通过匹配宽视场红外巡天探测器（WISE）数据、PS1以及在可能的情况下，SDSS和2微米全天巡天（2 MASS）物体来进行工作。 SVM算法将扩展到输出目录。 由此产生的星系图将比SDSS大几倍，比PS1少恒星污染，并且比WISE或2 MASS更深。 之前演示的机器学习工具将用于估计组合样本的光度红移。 新的PS1* 将是最宽的区域，最深的光度红移目录，并将是最佳的交叉相关研究与其他广泛的数据集，如CMB，X射线巡天，宇宙红外背景，引力透镜的地图。具体的问题，将研究包括CMB异常是否由LSS，是否超级结构将在PS1* 中发现有任何影响的CMB，如何从PS1* 创建的综合萨克斯-沃尔夫（ISW）效应图与普朗克卫星的CMB图相关，以及如何提取LSS统计和宇宙学参数。 该项目将探索用于恒星-星系分离和光度红移的新型机器学习技术，并应用最先进的统计技术，揭示宇宙学参数和暗能量。 它将调查LSS和CMB异常之间的任何联系，以及这是否与ISW一致或需要一个更奇特的理论。 该目录将公开提供给各种互相关研究，所开发的算法和开放源码软件将广泛传播。 这项研究通过课程，实习和包括跨学科的学生促进研究融入教学。

New statistical and machine learning techniques will create a better catalog of galaxies. This catalog will combine information from several existing surveys. A technique called "photometric redshifts" will give speed and distance information from study of the galaxy colors. The result will be the widest area and deepest catalog yet available. It will be ideal for comparing to other large area surveys, especially of the Cosmic Microwave Background (CMB). Those studies will shed light on cosmology and dark energy. They may even show whether a more exotic theory of gravity is needed. Education is included through special topic courses. There are connections to national laboratories. The methods will be introduced to students from other disciplines.This project will create a combined value-added galaxy catalog to be called PS1*, for statistical studies of large-scale structure (LSS). Building on experience developing a star-galaxy separation algorithm for the first PanSTARRS catalog (PS1) using Support Vector Machines (SVM) and training sets from Sloan Digital Sky Survey (SDSS) data, the work will proceed by matching Wide-field Infrared Survey Explorer (WISE) data, PS1, and where available, SDSS and Two Micron All Sky Survey (2MASS) objects. The SVM algorithm will extend over the output catalog. The resulting galaxy maps will be several times larger than SDSS, with less stellar contamination than PS1 alone, and will be deeper than WISE or 2MASS. Previously demonstrated machine learning tools will then be used to estimate photometric redshifts for the combined sample. The new PS1* will be the widest area, deepest photometric redshift catalog, and will be optimal for cross correlation studies with other wide data sets, such as the CMB, X-ray surveys, the cosmic infrared background, and maps of gravitational lensing.Specific questions that will be studied include whether CMB anomalies are caused by LSS, whether superstructures that will be found in PS1* have any effect on the CMB, how the map of the Integrated Sachs-Wolfe (ISW) effect to be created from PS1* relates to the CMB map from the Planck satellite, and how well LSS statistics and cosmological parameters can be extracted. The project will explore novel machine learning techniques for both star-galaxy separation and photometric redshifts and apply state of the art statistical techniques, shedding light on cosmological parameters and dark energy. It will investigate any connection between LSS and CMB anomalies, and whether this is consistent with ISW or requires a more exotic theory. The catalog will be publicly available for a variety of cross-correlation studies, and the algorithms and open source software developed will be disseminated widely. This study promotes the integration of research into teaching through courses, internships, and including cross-disciplinary students.