Nonparametric Methods for Detecting Large-Scale Structure

• Activity Number: 628
• Type: Invited
• Date/Time: Thursday, August 4, 2016 : 8:30 AM to 10:20 AM
• Sponsor: IMS
• Author(s): Yen-Chi Chen* and Christopher Genovese and Larry Wasserman and Peter Freeman and Shirley Ho
• Companies: Carnegie Mellon University and Carnegie Mellon University and Carnegie Mellon University and Carnegie Mellon University and Carnegie Mellon University
• Keywords: Nonparametric statistics ; Kernel density estimator ; astrostatistics ; density ridges

Abstract

The detection and characterization of filamentary structures in the cosmic web allows cosmologists to constrain parameters that dictates the evolution of the Universe. To detect cosmic filaments, we propose a density ridge model and use a nonparametric approach to estimate ridges of the underlying density. The density ridges are collection of curves that represent high density regions, which are excellent models for filaments. By comparing density ridges to both smoothed particle hydrodynamics simulation and the Sloan Digital Sky Survey, we found that several properties of galaxies, including principal axes, color, stellar mass and size, are influenced by filaments.