When Geometry and Statistics Meet Cosmology: the Challenge of Detecting Cosmic Webs.

When and Where

Monday, December 13, 2021 3:30 pm to 4:30 pm


Yen-Chi Chen


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 the cosmic web is often a complex process and so the quantification of its effect is non-trivial. Moreover, cosmic webs are complex structures and their detections in a large astronomy survey present a non-trivial computational challenge. In this talk, we will present geometric approaches in statistics and machine learning that show great potential in capturing the cosmic web and we will discuss both statistical and computational properties of these methods. We will show how these methods could detect cosmic webs in the Sloan Digital Sky Survey.

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About Yen-Chi Chen

Yen-Chi Chen is an assistant professor in the Department of Statistics and a data science fellow in the eScience Institute at the University of Washington. Dr. Chen received B.S. from National Taiwan University in 2011 and completed his PhD from Carnegie Mellon University in 2016 under the supervision of Christopher Genovese and Larry Wasserman. Dr. Chen has received several awards from the National Science Foundation and the National Institute of Health and is currently an Associate Editor of the Electronic Journal of Statistics and the Journal of American Statistical Association.

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