Counting voids and filaments: Betti Curves as a Topological Probe for Cosmology
astro-ph.CO
Submitted: 2025-12-08
Updated: 2026-09-08
Comments: 25 pages, 17 figures, published in PRD
DOI: 10.1103/1c23-8l95
Code: https://github.com/ajouellette/alpha_complex_wrapper
Project page: https://johannesbuchner.github.io/UltraNest
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- The 2dF Galaxy Redshift Survey: Final Data Release
- The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Large-scale Structure Catalogs for Cosmological Analysis
- The Baryon Oscillation Spectroscopic Survey of SDSS-III
- The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: Cosmological Implications from two Decades of Spectroscopic Surveys at the Apache Point observatory
- The Early Data Release of the Dark Energy Spectroscopic Instrument
- How Filaments are Woven into the Cosmic Web
- DESI 2024 VI: Cosmological Constraints from the Measurements of Baryon Acoustic Oscillations
- DESI DR2 Results II: Measurements of Baryon Acoustic Oscillations and Cosmological Constraints
- MUltiplexed Survey Telescope (MUST) Science White Paper I: Overview of Large-Scale Structure Cosmology in the Era of Stage-V Spectroscopic Surveys
- The Spectroscopic Stage-5 Experiment
- The Wide-field Spectroscopic Telescope (WST) Science White Paper
- DESI 2024 V: Full-Shape Galaxy Clustering from Galaxies and Quasars
- Planck 2018 results. IV. Diffuse component separation
- Cosmological Constraints using the Void Size Function Data from BOSS DR16
- Robust Morphological Measures for Large-Scale Structure in the Universe
- The Three--Point Correlation Function of the Cosmic Microwave Background in Inflationary Models
- Constraining primordial non-Gaussianity from the large scale structure two-point and three-point correlation functions
- Minkowski Functionals of Large-Scale Structure as a Probe of Modified Gravity
- Topological Data Analysis
- An introduction to Topological Data Analysis: fundamental and practical aspects for data scientists
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