no code implementations • 4 Aug 2023 • Jacky H. T. Yip, Adam Rouhiainen, Gary Shiu
The topology of the large-scale structure of the universe contains valuable information on the underlying cosmological parameters.
1 code implementation • NeurIPS Workshop TDA_and_Beyond 2020 • Alex Cole, Matteo Biagetti, Gary Shiu
We present a pipeline for characterizing and constraining initial conditions in cosmology via persistent homology.
no code implementations • NeurIPS Workshop TDA_and_Beyond 2020 • Alex Cole, Gregory J. Loges, Gary Shiu
We apply persistent homology to the task of discovering and characterizing phase transitions, using lattice spin models from statistical physics for working examples.
1 code implementation • 29 Sep 2020 • Alex Cole, Gregory J. Loges, Gary Shiu
This method suffices to identify magnetization, frustration, and vortex-antivortex structure as relevant features for phase transitions in our models.
Statistical Mechanics Algebraic Topology
1 code implementation • 10 Sep 2020 • Matteo Biagetti, Alex Cole, Gary Shiu
Persistent homology is a technique from topological data analysis that quantifies the multiscale topology of a data set, in our context unifying the contributions of clusters, filament loops, and cosmic voids to cosmological constraints.
Cosmology and Nongalactic Astrophysics High Energy Physics - Theory Algebraic Topology
no code implementations • 23 Jul 2019 • Alex Cole, Andreas Schachner, Gary Shiu
In this paper, we employ genetic algorithms to explore the landscape of type IIB flux vacua.