Large Scale Structure Studies on a Value Added Galaxy Catalog

University Of Hawaii, Honolulu

Investigators

Abstract

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.