TopoFisher optimizes trainable filtrations, vectorizations, and compressors in persistent homology to maximize Fisher information, yielding higher information than fixed cosmological summaries and approaching neural baselines with far fewer parameters while generalizing better under simulator shifts
Robust Morphological Measures for Large-Scale Structure in the Universe
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abstract
We propose a novel method for the description of spatial patterns formed by a coverage of point sets representing galaxy samples. This method is based on a complete family of morphological measures known as Minkowski functionals, which includes the topological Euler characteristic and geometric descriptors to specify the content, shape and connectivity of spatial sets. The method is numerically robust even for small samples, independent of statistical assumptions, and yields global as well as local morphological information. We illustrate the method by applying it to a Poisson process, a `double-Poisson' process, and to the Abell catalogue of galaxy clusters.
representative citing papers
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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TopoFisher: Learning Topological Summary Statistics by Maximizing Fisher Information
TopoFisher optimizes trainable filtrations, vectorizations, and compressors in persistent homology to maximize Fisher information, yielding higher information than fixed cosmological summaries and approaching neural baselines with far fewer parameters while generalizing better under simulator shifts
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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.