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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.15405 |
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| _version_ | 1866912594713903104 |
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| author | Calles, Juan Yip, Jacky H. T. Contardo, Gabriella Noreña, Jorge Rouhiainen, Adam Shiu, Gary |
| author_facet | Calles, Juan Yip, Jacky H. T. Contardo, Gabriella Noreña, Jorge Rouhiainen, Adam Shiu, Gary |
| contents | Building upon [2308.02636], we investigate the constraining power of persistent homology on cosmological parameters and primordial non-Gaussianity in a likelihood-free inference pipeline utilizing machine learning. We evaluate the ability of Persistence Images (PIs) to infer parameters, comparing them to the combined Power Spectrum and Bispectrum (PS/BS). We also compare two classes of models: neural-based and tree-based. PIs consistently lead to better predictions compared to the combined PS/BS for parameters that can be constrained, i.e., for $\{Ω_{\rm m}, σ_8, n_{\rm s}, f_{\rm NL}^{\rm loc}\}$. PIs perform particularly well for $f_{\rm NL}^{\rm loc}$, highlighting the potential of persistent homology for constraining primordial non-Gaussianity. Our results indicate that combining PIs with PS/BS provides only marginal gains, indicating that the PS/BS contains little additional or complementary information to the PIs. Finally, we provide a visualization of the most important topological features for $f_{\rm NL}^{\rm loc}$ and for $Ω_{\rm m}$. This reveals that clusters and voids (0-cycles and 2-cycles) are most informative for $Ω_{\rm m}$, while $f_{\rm NL}^{\rm loc}$ is additionally informed by filaments (1-cycles). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15405 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cosmology with Persistent Homology: Parameter Inference via Machine Learning Calles, Juan Yip, Jacky H. T. Contardo, Gabriella Noreña, Jorge Rouhiainen, Adam Shiu, Gary Cosmology and Nongalactic Astrophysics Machine Learning Algebraic Topology Building upon [2308.02636], we investigate the constraining power of persistent homology on cosmological parameters and primordial non-Gaussianity in a likelihood-free inference pipeline utilizing machine learning. We evaluate the ability of Persistence Images (PIs) to infer parameters, comparing them to the combined Power Spectrum and Bispectrum (PS/BS). We also compare two classes of models: neural-based and tree-based. PIs consistently lead to better predictions compared to the combined PS/BS for parameters that can be constrained, i.e., for $\{Ω_{\rm m}, σ_8, n_{\rm s}, f_{\rm NL}^{\rm loc}\}$. PIs perform particularly well for $f_{\rm NL}^{\rm loc}$, highlighting the potential of persistent homology for constraining primordial non-Gaussianity. Our results indicate that combining PIs with PS/BS provides only marginal gains, indicating that the PS/BS contains little additional or complementary information to the PIs. Finally, we provide a visualization of the most important topological features for $f_{\rm NL}^{\rm loc}$ and for $Ω_{\rm m}$. This reveals that clusters and voids (0-cycles and 2-cycles) are most informative for $Ω_{\rm m}$, while $f_{\rm NL}^{\rm loc}$ is additionally informed by filaments (1-cycles). |
| title | Cosmology with Persistent Homology: Parameter Inference via Machine Learning |
| topic | Cosmology and Nongalactic Astrophysics Machine Learning Algebraic Topology |
| url | https://arxiv.org/abs/2412.15405 |