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
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2roles
background 1polarities
background 1representative citing papers
A general non-perturbative field-level posterior is constructed and expanded around its Gaussian limit to express Fisher information in terms of connected correlators, recovering standard results for power spectrum and bispectrum while quantifying compression losses.
citing papers explorer
-
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
-
On the Relation Between Field-Level Posteriors, Correlators, and their Likelihoods
A general non-perturbative field-level posterior is constructed and expanded around its Gaussian limit to express Fisher information in terms of connected correlators, recovering standard results for power spectrum and bispectrum while quantifying compression losses.