Imprint of massive neutrinos on Persistent Homology of large-scale structure
M. H. Jalali Kanafi, S. Ansarifard, S. M. S. Movahed

TL;DR
This study uses Persistent Homology to analyze large-scale structure simulations, revealing how neutrino mass influences topological features and improving constraints on neutrino mass and cosmological parameters.
Contribution
It introduces a novel application of Persistent Homology to large-scale structure data for constraining neutrino mass and reducing parameter degeneracies.
Findings
Persistent Homology detects neutrino mass effects in topological features.
The method constrains neutrino mass with uncertainties of 0.0152 eV (m field) and 0.1242 eV (cb field).
Topological analysis helps reduce degeneracy between $M_{\nu}$ and $\sigma_8$.
Abstract
Exploiting the Persistent Homology technique and its complementary representations, we examine the footprint of summed neutrino mass () in the various density fields simulated by the publicly available Quijote suite. The evolution of topological features by utilizing the super-level filtration on three-dimensional density fields at zero redshift, reveals a remarkable benchmark for constraining the cosmological parameters, particularly and . The abundance of independent closed surfaces (voids) compared to the connected components (clusters) and independent loops (filaments), is more sensitive to the presence of for Mpc irrespective of whether using the total matter density field () or CDM+baryons field (). Reducing the degeneracy between and is achieved via Persistent Homology for the field but not…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsTopological and Geometric Data Analysis · Computational Physics and Python Applications · Black Holes and Theoretical Physics
