# Topological Data Analysis for Cosmology and String Theory

Friday, November 30th, 2018

Speaker:
Alex Cole

*University of Wisconsin-Madison*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.