# The persistence of large scale structures. Part I. Primordial non-Gaussianity

@article{Biagetti2020ThePO,
title={The persistence of large scale structures. Part I. Primordial non-Gaussianity},
author={Matteo Biagetti and Alex Cole and G. Shiu},
journal={Journal of Cosmology and Astroparticle Physics},
year={2020},
volume={2021}
}
• Published 2020
• Physics, Mathematics
• Journal of Cosmology and Astroparticle Physics
We develop an analysis pipeline for characterizing the topology of large scale structure and extracting cosmological constraints based on persistent homology. 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. We describe how this method captures the imprint of primordial local non-Gaussianity on the late-time… Expand
6 Citations
Reviews: Topological Distances and Losses for Brain Networks
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• 2021
This review paper surveys various topological distance and loss functions from topological data analysis (TDA) and persistent homology that can be used in brain network analysis more effectively. Expand
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• Physics
• 2020
Upcoming galaxy redshift surveys promise to significantly improve current limits on primordial non-Gaussianity (PNG) through measurements of 2- and 3-point correlation functions in Fourier space.Expand
Quantitative and Interpretable Order Parameters for Phase Transitions from Persistent Homology
• Physics, Mathematics
• 2020
We apply modern methods in computational topology to the task of discovering and characterizing phase transitions. As illustrations, we apply our method to four two-dimensional lattice spin models:Expand
Topological Echoes of Primordial Physics in the Universe at Large Scales
We present a pipeline for characterizing and constraining initial conditions in cosmology via persistent homology. The cosmological observable of interest is the cosmic web of large scale structure,Expand
Topological Echoes of Primordial Physics in the Universe at Large Scales
• Physics, Computer Science
• ArXiv
• 2020
Throughout this pipeline, the interpretability of topological features as input for statistical inference allows us to make contact with previous first-principles calculations and make new predictions. Expand

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