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Theory Seminar (High Energy/Cosmology)
Topological Data Analysis for Cosmology and String Theory
Date: Friday, November 30th
Time: 2:00 pm
Place: 5280 Chamberlin Hall
Speaker: Alex Cole, University of Wisconsin-Madison
Abstract: Persistent homology, the main technique underlying the field of Topological Data Analysis, computes the multiscale topology of a data set by using a sequence of discrete complexes. Roughly speaking, persistent homology allows us to compute the “shape” of data. In this talk I will introduce persistent homology and describe applications to data sets in cosmology and string theory. I will demonstrate how persistence diagrams provide an improved real-space observable for the Cosmic Microwave Background. In particular, persistence diagrams are more sensitive to local non-Gaussianity on a set of simulated temperature maps than Betti numbers, which are in turn more sensitive than the genus. I will also use persistent homology to characterize distributions of Type IIB flux vacua and as a framework for understanding the correlation of different low-energy features in moduli space.
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