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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Validates redshift-space power spectrum and bispectrum analysis on Abacus-PNG mocks to recover unbiased f_NL constraints for Euclid spectroscopic sample.
Forecasts from CSST ELG mocks at z=0.3, 0.6, 0.9 show joint power spectrum plus bispectrum analysis constrains f_NL to -20±52 in 1 (h^{-1} Gpc)^3 volume, with bispectrum improving precision by 5-6%.
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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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.
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Exploring Primordial Non-Gaussianity Measurements in the CSST Spectroscopic Survey
Forecasts from CSST ELG mocks at z=0.3, 0.6, 0.9 show joint power spectrum plus bispectrum analysis constrains f_NL to -20±52 in 1 (h^{-1} Gpc)^3 volume, with bispectrum improving precision by 5-6%.