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
The Cosmological Analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure
2 Pith papers cite this work. Polarity classification is still indexing.
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Validates redshift-space power spectrum and bispectrum analysis on Abacus-PNG mocks to recover unbiased f_NL constraints for Euclid spectroscopic sample.
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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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Euclid preparation: Testing multi-field inflation with galaxy power spectrum and bispectrum
Validates redshift-space power spectrum and bispectrum analysis on Abacus-PNG mocks to recover unbiased f_NL constraints for Euclid spectroscopic sample.