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VERSION:2.0
CALSCALE:GREGORIAN
PRODID:UW-Physics-TWaP
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SEQUENCE:2
UID:UW-Physics-Event-4932
DTSTART:20181130T200000Z
DTEND:20181130T212500Z
DTSTAMP:20200403T210205Z
LAST-MODIFIED:20181123T222141Z
LOCATION:5280 Chamberlin Hall
SUMMARY:Topological Data Analysis for Cosmology and String Theory\, Theory Seminar (High Energy/Cosmology)\, Alex Cole\, University of Wisconsin-Madison
DESCRIPTION: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.
URL:https://wp.physics.wisc.edu/twap/?id=4932
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