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Activity Number: 334 - Statistical Methods in Astronomy, Astrophysics and Cosmology
Type: Topic Contributed
Date/Time: Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
Sponsor: Astrostatistics Special Interest Group
Abstract #327301 Presentation
Title: Analyzing Cosmic Webs Using Geometric Approaches
Author(s): Yen-Chi Chen*
Companies: University of Washington
Keywords: cosmic web; manifold learning; clustering; nonparametric statistics; topological data analysis; astrostatistics
Abstract:

Matter in our Universe tends to aggregate around lower-dimension structures, weaving our Universe into a web-like structure known as the cosmic web. The cosmic web consists of several distinct substructures such as galaxy clusters, filaments, walls/sheets, and voids. The existence of the cosmic web has been observed in realistic sky surveys and computer simulations. Astrophysical theories have predicted the effect of the cosmic web on its nearby celestial bodies. However, testing these astrophysical theories is a non-trivial problem for several reasons. First, the precise definition of the cosmic web remains unclear -- we only know a few characteristics of these structures but there is no consensus on where the cosmic web starts and ends. Second, the effect of cosmic web is often a complex process and so the quantification of its effect is non-trivial. In this talk, we will present some geometric approaches in statistics that show great potential in capturing the cosmic web and may be used to test the related astrophysical theories.


Authors who are presenting talks have a * after their name.

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