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Topological Data Analysis
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Topological Data Analysis (TDA) can broadly be described as a collection of data analysis methods that find structure in data. This includes: clustering, manifold estimation, nonlinear dimension reduction, mode estimation, ridge estimation and persistent homology. This paper reviews some of these methods.
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Cited by 2 Pith papers
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Topological Signatures of Heating and Dark Matter in the 21 cm Forest
Persistence-based topological descriptors from 21 cm forest spectra provide complementary constraints on X-ray heating efficiency and warm dark matter free-streaming scale.
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Counting voids and filaments: Betti Curves as a Powerful Probe for Cosmology
Betti curves from persistent homology of large-scale structure provide complementary cosmological constraints on ns, sigma8, and Om, with tighter bounds when analyzed jointly with the power spectrum.
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