2000 character limit reached
Hierarchical Clustering in $Λ$CDM Cosmologies via Persistence Energy (2401.01988v2)
Published 3 Jan 2024 in astro-ph.CO, cs.CG, math.AT, and stat.ML
Abstract: In this research, we investigate the structural evolution of the cosmic web, employing advanced methodologies from Topological Data Analysis. Our approach involves leveraging LITE, an innovative method from recent literature that embeds persistence diagrams into elements of vector spaces. Utilizing this methodology, we analyze three quintessential cosmic structures: clusters, filaments, and voids. A central discovery is the correlation between \textit{Persistence Energy} and redshift values, linking persistent homology with cosmic evolution and providing insights into the dynamics of cosmic structures.
- The Aspen-Amsterdam void finder comparison project. mnras, 387(2):933–944, June 2008. doi:10.1111/j.1365-2966.2008.13307.x.
- R. Van de Weygaert and E. Platen. COSMIC VOIDS: STRUCTURE, DYNAMICS AND GALAXIES. International Journal of Modern Physics: Conference Series, 01:41–66, 2011. doi:10.1142/S2010194511000092. URL https://doi.org/10.1142/S2010194511000092.
- Evolution of the cosmic web. Monthly Notices of the Royal Astronomical Society, 441(4):2923–2973, 05 2014. ISSN 0035-8711. doi:10.1093/mnras/stu768. URL https://doi.org/10.1093/mnras/stu768.
- Persistent homology of the cosmic web – I. Hierarchical topology in Λnormal-Λ\Lambdaroman_ΛCDM cosmologies. Monthly Notices of the Royal Astronomical Society, 507(2):2968–2990, August 2021. ISSN 1365-2966. doi:10.1093/mnras/stab2326. URL http://dx.doi.org/10.1093/mnras/stab2326.
- Detecting and analysing the topology of the cosmic web with spatial clustering algorithms I: methods. Monthly Notices of the Royal Astronomical Society, 516(4):5110–5124, September 2022. ISSN 1365-2966. doi:10.1093/mnras/stac2444. URL http://dx.doi.org/10.1093/mnras/stac2444.
- W. E. Schaap and R. van de Weygaert. Continuous fields and discrete samples: reconstruction through Delaunay tessellations. aap, 363:L29–L32, November 2000. doi:10.48550/arXiv.astro-ph/0011007.
- R. van de Weygaert and J. R. Bond. Clusters and the Theory of the Cosmic Web. In M. Plionis, O. López-Cruz, and D. Hughes, editors, A Pan-Chromatic View of Clusters of Galaxies and the Large-Scale Structure, volume 740, page 335. 2008. doi:10.1007/978-1-4020-6941-3_10.
- M. E. Van Huffel and M. Palo. LITE: A Stable Framework for Lattice-Integrated Embedding of Topological Descriptors, 2024.
- Planck Collaboration. Planck 2018 results - VI. Cosmological parameters. AA, 641:A6, 2020. doi:10.1051/0004-6361/201833910. URL https://doi.org/10.1051/0004-6361/201833910.
- The IllustrisTNG Simulations: Public Data Release, 2021.
- First results from the TNG50 simulation: galactic outflows driven by supernovae and black hole feedback. Monthly Notices of the Royal Astronomical Society, 490(3):3234–3261, August 2019. ISSN 1365-2966. doi:10.1093/mnras/stz2306. URL http://dx.doi.org/10.1093/mnras/stz2306.
- First results from the TNG50 simulation: the evolution of stellar and gaseous discs across cosmic time. Monthly Notices of the Royal Astronomical Society, 490(3):3196–3233, September 2019. ISSN 1365-2966. doi:10.1093/mnras/stz2338. URL http://dx.doi.org/10.1093/mnras/stz2338.
- H. Edelsbrunner and D. Morozov. Persistent Homology. CRC Press, 3 edition, 2017. To appear.
- T. K. Dey and Y. Wang. Computational Topology for Data Analysis. Cambridge University Press, 2022. ISBN 9781009098168.
- The structure and stability of persistence modules. arXiv preprint arXiv:1207.3674, Mar 2013.
- V. Divol and T. Lacombe. Understanding the topology and the geometry of the space of persistence diagrams via optimal partial transport. arXiv preprint arXiv:1901.03048, 2019.
- A. Figalli and N. Gigli. A new transportation distance between non-negative measures, with applications to gradients flows with dirichlet boundary conditions. Journal de Mathématiques Pures et Appliquées, 94(2):107–130, 2010.
- V. Divol and T. Lacombe. Estimation and quantization of expected persistence diagrams, 2021.
- Yueqi Monod. Approximating persistent homology for large datasets. arXiv preprint arXiv:2204.09155, 2022.
- Efficient computation of persistent homology for cubical data. In Topological methods in data analysis and visualization II: theory, algorithms, and applications, pages 91–106. Springer, 2011.
- Computational homology. Applied Mathematical Sciences, 157, 2004.
- R. van de Weygaert and W. Schaap. The Cosmic Web: Geometric Analysis, page 291–413. Springer Berlin Heidelberg, 2008. ISBN 9783540447672. doi:10.1007/978-3-540-44767-2_11. URL http://dx.doi.org/10.1007/978-3-540-44767-2_11.
- The dtfe public software: The delaunay tessellation field estimator code, 2019.
- T. Sousbie. The persistent cosmic web and its filamentary structure - i. theory and implementation: Persistent cosmic web - i: Theory and implementation. Monthly Notices of the Royal Astronomical Society, 414(1):350–383, April 2011. ISSN 0035-8711. doi:10.1111/j.1365-2966.2011.18394.x. URL http://dx.doi.org/10.1111/j.1365-2966.2011.18394.x.
- P. Bubenik. The Persistence Landscape and Some of Its Properties, pages 97–117. 06 2020. ISBN 978-3-030-43407-6. doi:10.1007/978-3-030-43408-3_4.
- O. Hacquard. Statistical learning on measures: an application to persistence diagrams. arXiv preprint arXiv:2303.08456, 2023.
- Y. Cao and A. Monod. Approximating persistent homology for large datasets, 2022.