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Auteurs principaux: Yip, Jacky H. T., Biagetti, Matteo, Cole, Alex, Viswanathan, Karthik, Shiu, Gary
Format: Preprint
Publié: 2024
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Accès en ligne:https://arxiv.org/abs/2403.13985
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author Yip, Jacky H. T.
Biagetti, Matteo
Cole, Alex
Viswanathan, Karthik
Shiu, Gary
author_facet Yip, Jacky H. T.
Biagetti, Matteo
Cole, Alex
Viswanathan, Karthik
Shiu, Gary
contents Persistent homology naturally addresses the multi-scale topological characteristics of the large-scale structure as a distribution of clusters, loops, and voids. We apply this tool to the dark matter halo catalogs from the Quijote simulations, and build a summary statistic for comparison with the joint power spectrum and bispectrum statistic regarding their information content on cosmological parameters and primordial non-Gaussianity. Through a Fisher analysis, we find that constraints from persistent homology are tighter for 8 out of the 10 parameters by margins of 13-50%. The complementarity of the two statistics breaks parameter degeneracies, allowing for a further gain in constraining power when combined. We run a series of consistency checks to consolidate our results, and conclude that our findings motivate incorporating persistent homology into inference pipelines for cosmological survey data.
format Preprint
id arxiv_https___arxiv_org_abs_2403_13985
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Cosmology with Persistent Homology: a Fisher Forecast
Yip, Jacky H. T.
Biagetti, Matteo
Cole, Alex
Viswanathan, Karthik
Shiu, Gary
Cosmology and Nongalactic Astrophysics
High Energy Physics - Theory
Algebraic Topology
Persistent homology naturally addresses the multi-scale topological characteristics of the large-scale structure as a distribution of clusters, loops, and voids. We apply this tool to the dark matter halo catalogs from the Quijote simulations, and build a summary statistic for comparison with the joint power spectrum and bispectrum statistic regarding their information content on cosmological parameters and primordial non-Gaussianity. Through a Fisher analysis, we find that constraints from persistent homology are tighter for 8 out of the 10 parameters by margins of 13-50%. The complementarity of the two statistics breaks parameter degeneracies, allowing for a further gain in constraining power when combined. We run a series of consistency checks to consolidate our results, and conclude that our findings motivate incorporating persistent homology into inference pipelines for cosmological survey data.
title Cosmology with Persistent Homology: a Fisher Forecast
topic Cosmology and Nongalactic Astrophysics
High Energy Physics - Theory
Algebraic Topology
url https://arxiv.org/abs/2403.13985