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
Constraining M_ with the bispectrum
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New symmetry-based FFT estimators cut the cost of power-spectrum and bispectrum multipole measurements by roughly half while providing analytic shot-noise subtraction and an open Python package.
Validates redshift-space power spectrum and bispectrum analysis on Abacus-PNG mocks to recover unbiased f_NL constraints for Euclid spectroscopic sample.
citing papers explorer
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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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Efficient estimators for power spectrum and bispectrum multipole measurements
New symmetry-based FFT estimators cut the cost of power-spectrum and bispectrum multipole measurements by roughly half while providing analytic shot-noise subtraction and an open Python package.
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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.