Persistent homology in cosmic shear II: A tomographic analysis of DES-Y1

TL;DR

This paper introduces a novel application of persistent homology to cosmic shear data, providing the first cosmological constraints from topological data analysis in a tomographic survey, and demonstrates its effectiveness with DES-Y1 data.

Contribution

It extends topological data analysis to cosmology by applying persistent homology to cosmic shear, offering a new method for parameter inference and systematic marginalization.

Findings

Measured $S_8=0.747^{+0.025}_{-0.031}$ consistent with other probes.

Constrained intrinsic alignment parameter to $A=1.54\pm 0.52$, ruling out no alignments at 3σ.

First use of persistent homology for cosmological parameter constraints.

Abstract

We demonstrate how to use persistent homology for cosmological parameter inference in a tomographic cosmic shear survey. We obtain the first cosmological parameter constraints from persistent homology by applying our method to the first-year data of the Dark Energy Survey. To obtain these constraints, we analyse the topological structure of the matter distribution by extracting persistence diagrams from signal-to-noise maps of aperture masses. This presents a natural extension to the widely used peak count statistics. Extracting the persistence diagrams from the cosmo-SLICS, a suite of $N$-body simulations with variable cosmological parameters, we interpolate the signal using Gaussian Processes and marginalise over the most relevant systematic effects, including intrinsic alignments and baryonic effects. We find for the structure growth parameter $S_8=0.747^{+0.025}_{-0.031}$, which is in full agreement with other late-time probes. We also constrain the intrinsic alignment parameter to $A=1.54\pm 0.52$, ruling out the case of no intrinsic alignments at a $3\sigma$-level.