Search Results for author: Gary Shiu

Found 6 papers, 3 papers with code

Learning from Topology: Cosmological Parameter Estimation from the Large-scale Structure

no code implementations4 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.

Bayesian Inference

Topological Echoes of Primordial Physics in the Universe at Large Scales

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.

Interpretable Phase Detection and Classification with 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.

Classification General Classification +1

Quantitative and Interpretable Order Parameters for Phase Transitions from Persistent Homology

1 code implementation29 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

The Persistence of Large Scale Structures I: Primordial non-Gaussianity

1 code implementation10 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

Searching the Landscape of Flux Vacua with Genetic Algorithms

no code implementations23 Jul 2019 Alex Cole, Andreas Schachner, Gary Shiu

In this paper, we employ genetic algorithms to explore the landscape of type IIB flux vacua.

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