Is the observable Universe consistent with the cosmological principle?

PK Aluri, P Cea, P Chingangbam… - … and Quantum Gravity, 2023‏ - iopscience.iop.org
The cosmological principle (CP)—the notion that the Universe is spatially isotropic and
homogeneous on large scales—underlies a century of progress in cosmology. It is …

[HTML][HTML] Vitreous carbon, geometry and topology: A hollistic approach

P Mélinon - Nanomaterials, 2021‏ - mdpi.com
Glass-like carbon (GLC) is a complex structure with astonishing properties: isotropic sp 2
structure, low density and chemical robustness. Despite the expanded efforts to understand …

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

S Heydenreich, B Brück, P Burger… - Astronomy & …, 2022‏ - aanda.org
We demonstrate how to use persistent homology for cosmological parameter inference in a
tomographic cosmic shear survey. We obtain the first cosmological parameter constraints …

A streamlined quantum algorithm for topological data analysis with exponentially fewer qubits

S McArdle, A Gilyén, M Berta - arxiv preprint arxiv:2209.12887, 2022‏ - arxiv.org
Topological invariants of a dataset, such as the number of holes that survive from one length
scale to another (persistent Betti numbers) can be used to analyse and classify data in …

On the stability of persistent entropy and new summary functions for topological data analysis

N Atienza, R González-Díaz, M Soriano-Trigueros - Pattern Recognition, 2020‏ - Elsevier
Persistent homology and persistent entropy have recently become useful tools for patter
recognition. In this paper, we find requirements under which persistent entropy is stable to …

Finding cosmic voids and filament loops using topological data analysis

X Xu, J Cisewski-Kehe, SB Green, D Nagai - Astronomy and Computing, 2019‏ - Elsevier
We present a method called Significant Cosmic Holes in Universe (SCHU) for identifying
cosmic voids and loops of filaments in cosmological datasets and assigning their statistical …

Persistent homology in cosmic shear: constraining parameters with topological data analysis

S Heydenreich, B Brück, J Harnois-Déraps - Astronomy & Astrophysics, 2021‏ - aanda.org
In recent years, cosmic shear has emerged as a powerful tool for studying the statistical
distribution of matter in our Universe. Apart from the standard two-point correlation functions …

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

M Biagetti, A Cole, G Shiu - Journal of Cosmology and …, 2021‏ - iopscience.iop.org
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 …

Graphcode: Learning from multiparameter persistent homology using graph neural networks

F Russold, M Kerber - Advances in Neural Information …, 2025‏ - proceedings.neurips.cc
We introduce graphcodes, a novel multi-scale summary of the topological properties of a
dataset that is based on the well-established theory of persistent homology. Graphcodes …

Topological data analysis for the string landscape

A Cole, G Shiu - Journal of High Energy Physics, 2019‏ - Springer
A bstract Persistent homology computes the multiscale topology of a data set by using a
sequence of discrete complexes. In this paper, we propose that persistent homology may be …