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2020

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

Biagetti, Matteo, Cole, Alex, Shiu, Gary

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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 distribution of dark matter halos, using a set of N-body simulations as a proxy for real data analysis.
For our best single statistic, running the pipeline on several cubic volumes of size $40~(\rm{Gpc/h})^{3}$, we detect $f_{\rm NL}^{\rm loc}=10$ at $97.5\%$ confidence on $\sim 85\%$ of the volumes.

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